Brocke Institute of Information systems University of Liechtenstein Vaduz Liechtenstein Theresa Schmiedel Institute of Information systems University of Liechtenstein Vaduz Liechtenstein ISSN 2192-8096 ISSN 2192
and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
as well as v the whole team of the Institute of Information systems for their excellent work in preparing the conference and making every guest feel comfortable during their visit at our University.
and manual labor (workflow), leading ultimately to cost-resilient processes. vii However, in the age of digitization,
and innovation to BPM will ultimately lead to a new class of (process-aware) information systems,
51 Sandy Kemsley Leveraging Social media for Process Innovation. A Conceptual Framework...59 Peter Trkman and Monika Klun The Role of Enterprise Systems in Process Innovation...
Lessons Learned from a Smartphone-Based Insurance Telematics Initiative...85 Jens Ohlsson, Peter Haâ ndel, Shengnan Han,
and Richard Welch Part III Driving Innovation Through Advanced Process Analytics Extracting Event Data from Databases to Unleash Process Mining...
Based on these insights, we provide an outlook on the chapters of this book which may guide both the research
and practice of BPM in driving innovation in a digital world. 1 Introduction Information technology (IT) plays a vital role in driving innovation in today†s digital world,
such as mobile and real-time technologies, the Internet of things, big data analytics, and social media, have come to the fore in recent years,
only the incorporation of IT into business processes allows T. Schmiedel(*)•J. vom Brocke Institute of Information systems, University of Liechtenstein, Fuâ rst-Franz-Josef-Str. 21,9490 Vaduz
we provide a brief outlook on the chapters of this book. 2 The Need for Innovation Innovation is a concept that seems to enter more and more business-and management-related discussions.
, operations in and across organizational functions, are at the core of every organization, the relevance of BPM for companies of all kinds of industries, private,
Innovations that have a substantive influence on many or even all parts of the economy (e g. the Internet.
i e. innovations as seen from the user perspective. While the importance of product innovations is undisputed,
or itunes. These are largely Fig. 2 Product vs. process innovation 6 T. Schmiedel and J. vom Brocke successful businesses built around products that seem to have been out there forever, namely,
smartphones serve as a good example to illustrate the shift in the rate of innovation across time.
Smartphones represent a product innovation combining various functionalities such as the ones of mobile phones web browsers, or navigation systems.
When the first smartphones entered the market, the rate of this product innovation was very high.
Meanwhile, however, this product innovation has led to countless process innovations in both private and business life, ranging from individual assistance (e g. on health care) to corporate app stores innovating sales processes for instance.
Even though smartphones as such are not highly innovative any more, they still enable manifold process innovations in all kinds of application areas.
New technologies including mobile and real-time technologies the Internet of things, big data analytics, and social media clearly illustrate the enormous impact of IT on society in terms of enabling competitiveness and welfare (vom Brocke, Debortoli, Muâ ller, & Reuter, 2014).
Further, examining such technologies gives an indication how strongly IT generally shapes our times. The digital age is characterized increasingly by usage of the Internet through anyone and anything at anytime and anywhere:
•Anyone and Anything: Addressing the question on who represents the digital age, we can observe that large parts of modern societies are experts in using IT in their daily business.
Looking at new generations growing up with the Internet, i e. so-called digital natives, their expertise with IT is advanced even more,
working with the Internet comes ever more naturally to them and is increasingly taken for granted. Not only people are online today,
however, as nearly anything can be connected to the Internet, including cars, houses, clothes, tools, and Business Process Management:
since 2013 more â€oethings†are on the Internet than people (Mclaughlin, 2013). The possible connection of anyone and anything to the Internet is a key characteristic of the digital age. •Anywhere:
Another key characteristic of the digital age refers to the ubiquity of the Internet. Technically it is possible to realize a comprehensive network coverage that enables Internet access around the globe.
Internet providers for such services are omnipresent and Internet-togo use is growing as it becomes more and more affordable.
Being able to go online anywhere can fundamentally change social and economic processes. Potentially, ubiquitous Internet access might increase efficiency as waiting
and travelling times can be used effectively. For example, Homeplus has innovated the retail market in South korea by placing QR-code-based shopping experiences in local underground transport
Another characteristic of the digital age relates to the fact that data is not only available anywhere (irrespective of location) but also anytime (irrespective of time.
Particularly, it also relates to the idea of real-time avail-ability of data. The possibility to receive up-to-date information at any point in time is key for essential innovations in many business processes.
Integrating multiple kinds of real-time data, analytics today already enables the prediction of events like the spread of the flu
It is intriguing to think how such data integration will innovate our professional and private lives in the near future,
or to rest for a few minutes based on body data taken from the skin (vom Brocke,
Online platforms, social media, and mobile apps, for example, are used increasingly to technologically support collective efforts to develop new products and services,
including the redesign of process steps through integrating IT products such as smart phones and tablets or IT services such as mobile apps.
mobile phone apps that allow for new sales processes, and big data analytics that allow for real-time process decisions based on data available from products in use.
Overall, we can observe distinct ways in which BPM can serve as a source of innovation.
However, we might observe a shift in one of the core institutional logics that BPM professionals draw from:
for example, meanwhile allow for real-time mining of business processes based on the digital traces that single process steps leave or based on text mining possibilities (Guâ nther, Rinderle-Ma, Reichert, Van der
outlining how mobile, cloud, social, and analytical technologies initiate change in the nature of work and
Peter Trkman and Monika Klun report on â€oeleveraging Social media for Process Innovation. A Conceptual Frameworkâ€.
and J. vom Brocke how social media can be used in various phases of business process life cycles to support, for example, the modeling, execution, monitoring and improvement of organizational processes.
and enforcer in organizational innovations and illustrates this by the opportunities of cloud computing for the integration of enterprise systems in process innovations.
Lessons Learned from a Smartphone-Based Insurance Telematics Initiativeâ€. They present the potentials of behavioral-based insurance
and emphasize the need for process changes in organizations to leverage the potentials of insurance telematics.
Four chapters present latest findings on the role of analyzing extant data for realizing innovations in a process context.
Wil van der Aalst reports on â€oeextracting Event Data from Databases to Unleash Process Miningâ€. He introduces an approach to create event logs from underlying databases as a fundamental prerequisite for the application of process-mining techniques
when information systems do not explicitly record events. Jan Recker gives insights on â€oeevidence-Based Business Process Management:
Using Digital Opportunities to Drive Organizational Innovationâ€. He illustrates how digital capabilities enable organizations to innovate based on facts rather than fiction
They show how analyzing process execution logs offline can detect deviant behavior that leads to performance changes
Digital innovation as a fundamental and powerful concept in the information systems curriculum. Management Information systems Quarterly, 38 (2), 329†354.
Guâ nther, C.,Rinderle-Ma, S.,Reichert, M.,Van der Aalst, W. M. P, . & Recker, J. 2008).
Introduction, methods and information systems (Vol. 1, pp. 3†16. Berlin: Springer. 14 T. Schmiedel and J. vom Brocke Kemsley, S. 2015.
Leveraging crowdsourcing: Activation-supporting components for IT-based ideas competition. Journal of Management Information systems, 26 (1), 197†224.
Mclaughlin, D. 2013, June 17. More mobile devices than people by the end of 2013 †is prepared your business?
Cisco Blog. Muâ ller, O.,Schmiedel, T.,Gorbacheva, E, . & vom Brocke, J. 2014). Toward a typology of business process management professionals:
Identifying patterns of competences through latent semantic analysis. Enterprise Information systems, 8, 1†31. Recker, J. 2015.
Evidence-based business process management: Using digital opportunities to drive organizational innovation. In J. vom Brocke & T. Schmiedel (Eds.
The six core elements of business process management. In J. vom Brocke & M. Rosemann (Eds.
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Information systems and E-business Management, 9 (4), 407†446. Smith, H, . & Fingar, P. 2004, July).
Communications of the Association for Information systems, 34 (1), 151†167. vom Brocke, J.,Petry, M,
Application strategies for neuroscience in information systems design science research. Journal of Computer Information systems, 53 (3), 1†13. vom Brocke, J,
. & Rosemann, M. Eds.).2015). ) Handbook on business process management (2nd ed.).Berlin: Springer. vom Brocke, J.,Schmiedel, T.,Recker, J.,Trkman, P.,Mertens, W,
The fourth industrial revolution is enabled by the introduction of the Internet of things and Services into the manufacturing environment.
In the future, businesses will establish global networks that incorporate their machinery, warehousing systems and production facilities in the shape of Cyber-Physical Systems (Lee, 2008.
Existing big data technology can make information available on a real-time basis and at the same time enable prediction of future events,
and processing the data in real-time is known. However the existing organization and business processes becomes a barrier for improvements.
and software tools. 3. 1 Proactive Value Chains in a Process Innovation Perspective Digital and flexible manufacturing has been around for many years.
In the past, the concept was referred to as Computer Integrated Manufacturing (CIM) and even though the original idea was innovative,
In general the availability of data is never an issue. The usability of data on the other hand hinders the concepts from flourishing in the factories.
Interoperability is consistently an issue. Visibility in the value chain is a prerequisite for a proactive reaction.
Enterprise resource planning (ERP) systems and Data warehouses (DW) as illustrated in Fig. 3 below. The data processing, from the time an event occurs in manufacturing (for example a measurement of quality data) until management is able to make sense of the event and its consequences
requires the aggregation on information through several systems layers as illustrated below. Even though the information is available,
We want to be able to make decisions based on real time data, which can be done using, e g.,
, in-memory database technologies. To sum up and to frame the research challenges of proactive value chains:
consistent and managed applications of models Dispersed intelligence Distributed intelligence Data, information, knowledge, models and expertise are used available
The core function of the MADE Open Factory is the ability to experiment with new business processes enabled by advanced process technology.
Third, the new technologies, like Internet of things, require that manufacturing broaden its perspective: from the factory floor towards the entire life cycle of a product or service.
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https://smartmanufacturingcoalition. org/sites/default/files/spm -an operations and technology roadmap. pdf Welke, R. J. 2015. Thinking tri-laterally about business processes, services and business models:
ineffective and/or misaligned with client/users needs. Any attempt to preemptively or reactively respond to market change
while rather narrow services such as an order-approval, database request, or an ERP-based shipping receipt event entry are at the other end.
It offers a mobile phone voice and data service to its targeted customers (primarily teens and young adults) consistent with its youthful, innovative brand.
To offer this service (sign up to ongoing voice/data provisioning) it could have created all the secondary,
tertiary and lower level services associated with payments, accounting, network connectivity, etc. Instead, it has chosen to wire together (compose) existing services from other service providers to achieve the bulk of its â€oeend-to-end†service offering to its customers,
such as those in information technology, it could mean a very well-defined interface definition that, when correctly invoked and initiated, returns a pre-specified set of values based upon an equally well-defined set of input values.
At the other end of the spectrum are designed services to respond to prospective users (clients) with vaguely defined/formalized needs that
the organizational area served by the services, it†s granularity, its mode or channel of delivery (e g. web-based, walk-in bricks-and-mortar, etc.
For example, a difficult â€oeinventory problem†solution can also be looked at as a mixed integer-programming problem.
Or either of these perspectives on the problem might be restated as a dynamic programming or systems dynamics simulation problem.
relations Procurement Service fulfillment Compensation Invoicing Product data management Service provisioning Component fabrication IT service management Product design & development Shipping Corporate communications Knowledge management
Examples abound, such as Apple†s introduction of the ipod and smartphone, or Skype†s introduction of consumer VOIP.
And, of course, the Internet and the World wide web. What made many of these more compelling is that they represent services as platforms for other services (and thus additional innovation) and
An appropriately formulated Google search can easily identify a range of offerings for a particular PTBS that â€oecompete†with an organization†s internal and external service offering (s). The key issues here are:
We previously noted Virgin Mobile as one that has done this masterfully. But many other examples abound.
European Journal of Information systems, 19 (3), 359†376. Alter, S. 2008. Service system fundamentals: Work system, value chain, and life cycle.
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Communications of the Association for Information systems, 16 (1), 1†25. Osterwalder, A.,Pigneur, Y.,Bernarda, G,
Not specific to BPM, these are transforming both consumer and enterprise software; these are described next to provide context for the following sections on BPM technologies. 2. 1 Mobile and Cloud Mobile and cloud,
although they can be implemented independently, are related often since many mobile solutions also depend on public cloud infrastructure.
On the surface, mobile and cloud are just deployment platforms: mobile is the platform for the end user,
while cloud is the platform for serving the end-user functionality. Both, however, are transformative technologies
Mobile has become mainstream for consumer applications†finding when the next bus is coming while you are walking to the station,
or using your phone to pay at your favorite coffee shop†but is also making inroads with remote and mobile enterprise workers.
A healthcare worker working with patients in their homes can gather patient information on a mobile device
removing the need to re-enter data when they return to their office, and receive immediate feedback on potential drug interactions and suggested next steps.
An industrial site inspector can input inspection data directly, triggering maintenance requests. Enterprise mobile applications can improve efficiency
whether accessed via a mobile device or a traditional computer, allow anyone to participate from anywhere:
employees from home or remote corporate offices, or business partners and customers from their own 52 S. Kemsley location.
Since cloud applications typically do not require licensing and installation on the user†s computer or mobile device,
and easy shared between users. 2. 2 Social Collaboration and Distributed Co-creation Enterprise social collaboration typically takes one of two forms:
For maximum benefit, the social aspect is integrated directly into the core business applications that people are using,
Big data and Analytics Information-filled events are generated by a wide variety devices and systems: computers, mobile phones, vehicles, industrial equipment, sensors, security systems, building automation systems,
and even social networks such as Twitter. The result is a flood of data that may contain valuable information,
if that information can be detected. Information gleaned from events may allow for real-time preemptive problem detection and resolution,
by finding correlated sequences of events and applying predictive analytics to determine that a problem is likely to occur in the future,
or user alerts to avoid the problem. Aggregated events from a longer period of time can be analyzed to detect patterns of behavior
A variety of data-focused technologies are combined to achieve these goals, including complex event processing, pattern analysis and detection, big data processing, predictive analytics and automated decisioning.
Emerging Technologies in BPM 53 3 The Changing Nature of Work The nature of work is changing:
and organizations work. 3. 1 Social BPM Consumer social software, first identified in the early 2000s, has a defining characteristic of harnessing collective intelligence by allowing user-created content and collaboration.
This raised user expectations for enterprise software: today†s workers expect to be able to configure their own environment to suit their working style,
to collaborate with others at any point where they see fit, and to combine information from multiple internal and external sources
For example, an internal social network that allows employees to create profile pages can be used for locating others with specific skills
and web-based tools facilitate collaboration across business units and with other organizations. As the 54 S. Kemsley community forms around the collaborative process discovery tools, new uses will be discovered for process discovery and management,
where a user can add collaborators to his assigned task by expanding the visibility of that task to others based on his tacit knowledge of their skills and experience,
this captures a record of the collaboration, including who was involved in decision-making on each process instance. •Activity stream user interfaces,
Users define their own subscriptions and alerts to fine-tune the flood of information, allowing for better identification and management of their important tasks;
and allows the stream to be formatted for a mobile device, allowing process monitoring via event streams by anyone on the monitoring platform of their choice.
the user can define them for that instance without changing the underlying model on which new instances will be based.
Using predetermined process models, historical data from the executing and past processes, and simulation techniques to project forward from 56 S. Kemsley a point in time,
runtime simulation can compare â€oewhat-if†scenarios to determine optimal preemptive actions based on the current context of the process instance and historical data for similar instances.
Introduction, methods and information systems (Vol. 1, pp. 3†16. Berlin: Springer. Muâ ller, O.,Schmiedel, T.,Gorbacheva, E,
Identifying patterns of competences through latent semantic analysis. Enterprise Information systems, 8, 1†31. Ohlsson, J.,Haâ ndel, P.,Han, S,
Lessons learned from a smartphone-based insurance telematics Emerging Technologies in BPM 57 initiative. In J. vom Brocke & T. Schmiedel (Eds.
The six core elements of business process management. In J. vom Brocke & M. Rosemann (Eds.
Introduction, methods and information systems (Vol. 1, pp. 109†124. Berlin: Springer. Schenk, B. 2015. The role of enterprise systems in process innovation.
The match of business process management and social media †A conceptual framework. In J. vom Brocke & T. Schmiedel (Eds.
Communications of the Association for Information systems, 34 (1), 151†167. vom Brocke, J.,Schmiedel, T.,Recker, J.,Trkman, P.,Mertens, W,
Business Process Management Journal, 20 (4), 530†548.58 S. Kemsley Leveraging Social media for Process Innovation.
Potentials for achieving this lie in social media, as an increasingly popular option in the digital world with which to involve the creativity and opinions of various stakeholders from both within and outside an organization.
Yet, it is still not well researched how companies can harness the various benefits for using social media to better involve both employees and customers in various phases of the business process life cycle.
We propose a conceptual framework that enables the classification of various types of social media use (e g. within organization
and continuity are social media (†SM€), as an increasingly popular option in the digital world with which to involve the creativity and opinions of various stakeholders from both within and outside an organization (Kaplan & Haenlein, 2010;
SM are a group of Internet-based applications that build on the ideological and technological foundations of Web 2. 0,
and exchange of user generated content (Kaplan & Haenlein, 2010). They can be of different types:
blogs, social networking sites (e g. Facebook), collaborative projects (e g. wikis), content communities (e g. Youtube), virtual social worlds (e g.
Second life) and virtual game worlds (e g. World of Warcraft. Kane, Alavi, Labianca, and Borgatti (2014) define SM as information technologies that support interpersonal communication
and collaboration using Internet-based platforms. We here understand SM to be a service that facilitates networking among employees and stakeholders,
regardless whether this solely includes internal, or also encompasses external stakeholders. Several authors have discussed already coupling strategies, benefits,
and the requirements for successful implementation of SM (Bruno et al.,2011; Schmidt & Nurcan, 2009;
Silva et al. 2010). ) Yet, as noted by Trkman and Trkman (2011) the purpose of SM needs to be identified clearly before the start of SM implementation.
2011) introduce a new paradigm of the life cycle of business processes that enables agile business process management by applying social media in the business process life cycle.
social software allow for an â€oea posteriori control of quality†(Bruno et al.,2011). ) Our paper builds on the research by Bruno et al. by adding to their paradigm
2010) identify the main advantages of using SM for BPM such as integration of users into BPM,
and Reijers (2010) discuss social networks and their proximity as a possibility of sharing and exchanging process models.
like the demand of users for â€oeinstant gratification, rich user experiences and rapid access to informationâ€.
SM, especially web-based, represent a communication tool of choice for many organizations†the powerful
Social networking tools provide intensified collaboration among all stakeholders by providing a common network for interaction,
structuration and organization of information by entire communities of interacting users as opposed to predetermined specialists. •Continuous aggregation:
Absence of separation between content contributors and consumers as well as low input efforts mean lowered thresholds for contributing data and knowledge. •Continuous assessment:
The contributions are under constant and recursive assessment by all users, so errors can be identified
expediting execution and Leveraging Social media for Process Innovation. A Conceptual Framework 61 adding customer value (Dumas, La Rosa, Mendling, & Reijers, 2013;
SM present a tool that can enable the users to â€oestep outside†the structured process and initiate an â€oead hoc collaboration†(Kemsley, 2010.
or risks of data loss, may prevent organizations from (successfully) implementing SM in a business process life cycle (Kemsley, 2010).
users tend to migrate†in often unpredictable ways†to new tools, and the reasons for content contribution are highly diverse (Quan-Haase, 2007).
Data put online can quickly go viral. A typical case of the â€oevirulence†and unpredictability of SM is the United airlines breaks guitars video clip
or information, could be used as incriminating 62 P. Trkman and M. Klun data in court proceedings.
A lack of responsiveness from users can undermine the successful implementa-tion of SM. It can be brought about by unclear expectations (regarding both the purpose and use of SM as well as project execution) and also by a lack of motivation.
or governing authority is the characteristic of general SM like Facebook, but can prove too passive for the business environment.
Some companies and organizations are already blocking the access to such sites (Frosch, 2007), but studies show that SM adapted to an organization setting can provide substantial benefits for organizations (Sena & Sena, 2008).
1) increase awareness of all stakeholders regarding Leveraging Social media for Process Innovation. A Conceptual Framework 63 process modeling and execution,(2) aggregate information, relevant for process modeling by different participants,
The monitoring phase can benefit from including SM for (1) receiving the (quantitatively measured) data and feedback from all stakeholders of the network and (2) sharing the process performance results with co-workers and customers/end-users alike.
(3) statistical analysis of SM data to provide possibilities for process improvement. 4. 1 Modeling Phase for Internal Participants The modeling of business processes provides a shared and comprehensive understanding of the business
Employees are involved actively in preparing the process model by contributing the needed data or knowledge,
which enable communication and collaboration among all employees by providing job-specific tools and applications on the intranet IBM-News, 2006).
Other users are included in the process itself, since they are aware of the project activities
Table 1 A framework for classification of SM inclusion in business process life cycle Internal participants External participants Process modeling phase Involving the employees in process modeling Gathering data
and evaluating ideas for process improvement from stakeholders Leveraging Social media for Process Innovation. A Conceptual Framework 65 4. 2 Modeling Phase for External Participants Organizations today strive to be customer-centric
present and share data or artifacts (video, pictures or other forms of non-textual content).
the most affluent contributors being ranked as top users. The use of SM in execution of business processes can bring about organizational changes as well.
which used Facebook to communicate with passengers during a natural disaster. When the Eyjafjallajoâ kull volcano erupted in May 2010 flights were cancelled at most European airports.
TAP was able to reach a much wider audience via Facebook instead of one customer at a time via the call center (Vaz Vieira & Jaklic, 2013.
In turn â€oesuppliers†can access these sites and contribute innovation ideas. An example thereof is the connect+develop site of Proctor and Gamble;
a networking base for outsourcing process development (Proctor&gamble, 2014. Among other inventions, the site enabled expedited development of their pulsating toothbrush,
which was only an idea at the time and would have needed up to 5 more years in development.
found through their open innovation site, the joint research and development effort resulted in the product being on the market in a single year.
One way of using SM in the recruitment efforts is for companies to inspect popular sites
such as Facebook, for additional information about the candidate. In such processes SM are applied as an evaluation device,
All participants should have access to the monitored data and thus, in some way receive feedback about the business process they are participating in
Leveraging Social media for Process Innovation. A Conceptual Framework 67 The acquired feedback during the monitoring phase gives information on the appropriateness of a process and its execution.
Gathering the data required for the analysis can be time-consuming and fragmentary. Achieving a high response rate with surveys
and similar data gathering tools can be challenging. The already existing involvement of users in SM can simplify data contribution.
Including SM in the monitoring process provides stakeholders throughout the organization with a chance to contribute
4. 6 Monitoring Phase for External Participants One possibility of incorporating SM in the monitoring phase is also to make acquired data publicly accessible.
The statistical analysis of available data flows and other SM measures enable the evaluation of alternative process designs.
to user needs, often designed by users themselves and allow many types of content to evolve through a wide variety of collaborative processes (Von Krogh, 2012).
SM can be used to involve active participation in the process improvement phase, since customers and business partners can submit
while at the same time using SM to infuse flexibility in all phases of a business Leveraging Social media for Process Innovation.
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Leveraging Social media for Process Innovation. A Conceptual Framework 73 The Role of Enterprise Systems in Process Innovation Bernd Schenk Abstract Process innovation†redefining the way of doing business†is of paramount importance for the sustainable success of organizations.
The ambiguity of opportunities offered by new technology is illustrated by the example of the cloud computing paradigm.
The findings refer to data integration and business process support as the main benefits of enterprise systems
B. Schenk(*)Institute of Information systems, University of Liechtenstein, Fuâ rst-Franz-Josef-Str. 21,9490 Vaduz, Liechtenstein e-mail:
one core area of process innovation must lie in the embedding of technology in an organization,
Therefore, every new software release is an opportunity for process innovation in an organization and a challenge to take maximum advantage of this opportunity. 2 Different Roles of Enterprise Systems in Process Innovation The following section describes the main roles that an enterprise system can have in a process innovation scenario.
This scenario relates to changes in the enterprise system (e g. new software releases and adding mobile computing components) triggering the process innovation.
Additional capabilities of the system allow a new way of doing business. Process innovation is triggered within the enterprise system in this scenario.
They back up a process innovation initiative by the changed capabilities of a new system component to achieve increased acceptance of The Role of Enterprise Systems in Process Innovation 77 changing routines by the end user.
as the embedment in enterprise systems (e g. mobile computing, in-memory computing, cloud computing) is a possible source of process innovation.
including the invocation of software services by an activity. This new design paradigm enables a detailed adaptation of the software
and implementation of process innovation while using the standard methods provided. However, enterprise systems are only able to provide value contributions
Implications of Cloud computing The following section illustrates the complexity and ambiguous opportunities an organization is confronted with
It exemplarily highlights the potentials of cloud computing paradigm adoption based on company size as classification criterion. 78 B. Schenk Cloud computing has been a buzzword in the area of enterprise computing for some years now.
However, the expectations towards the implementation of a cloud computing model for an organization†s enterprise system are ambiguous.
In many cases cloud computing is understood as a pure cost-cutting measure which enables an easier operation of enterprise systems.
a cloud computing provider can deliver higher performance at lower cost compared to on-premise (in-house) operation models.
Cloud computing is understood therefore as new generation outsourcing within many organizations (Salleh, Teoh, & Chan, 2012.
Another field of application is the implementation of an enterprise systems extension, like customer relationship management software or the establishment of a common integration platform along a supply chain.
Cloud computing is used, moreover for integration of new technology while using standardized platforms. Cloud computing enables, inter alia,
integration of in-memory computing and mobile device access to enterprise systems. Integration can be achieved much easier in the cloud by using the existing infrastructure of a cloud solution provider than by implementation in conventional on-premise solutions.
The examples given above show the different expectations towards cloud computing deployment. While it is a clear cost-cutting measure
when it is considered an outsourcing activity, it can be a trigger for business process innovation,
It is therefore of paramount importance to take a closer look at the details of the cloud computing model and especially its service models.
Although there is no common definition of cloud computing and its components, the NIST definition of cloud computing (Mell & Grance,
when considering the opportunities and consequences of cloud computing for an enterprise, the service and deployment models as given in Fig. 1 should receive more attention.
In many cases the umbrella term cloud computing is used and no further distinction is made between either different service models or deployment models.
This causes ambiguous expectations towards the cloud computing paradigm which lead to fuzzy assumptions about cloud computing†s potential value contribution in an organization.
In this example a special focus should be placed on the usage of cloud computing in the sector of highly integrated enterprise systems.
these systems differ from other IT solutions as they (1) support the core business processes of an organization,(2) show a high degree of horizontal and vertical integration on different system layers,
The challenges of process innovation in The Role of Enterprise Systems in Process Innovation 79 relation to cloud computing are illustrated by focusing on different service models,
Cloud computing is considered to be a new delivery model enabling a focus on core competences while outsourcing the IT-related activities to professional cloud sourcing providers.
Software vendors and consulting companies subsume many different applications and solution packages under this umbrella term.
The table shows how diversified the implications of cloud computing for an organization are. Coming back to our claim that the usage Fig. 1 NIST model of cloud computing (Mell & Grance,
2011) 80 B. Schenk of unclear terminology could lead to unsatisfactory outcomes of an organization†s process innovation initiative,
the example of cloud computing shows the potential of severe mismatches of expectations and outcomes. A lack of knowledge and understanding of new paradigms like cloud computing and their applicability to enterprise systems might cause obstacles to process innovation in an organization.
Enterprise systems have shown a low frequency of change in the past. Companies try to keep the system in operation
or integrating new technologies like in-memory computing or the cloud computing paradigm. What Weick (1977) calls a chronically unfrozen system in management theory can be transferred to the area of enterprise systems as a new modus operandi.
Enterprise systems are considered to be at the core of enterprise operations and therefore follow the dynamics of organizational change.
Fast changing environments, like value webs as a form of inter-organizational cooperation, increase the frequency of change for both an organization and its systems,
and a Fig. 3 Potential roles of enterprise systems in process innovation while adopting the cloud computing paradigm 82 B. Schenk platform for innovation enablement in an organization.
This transformation process is supported by achievements such as new technologies (e g. in-memory computing enabling real-time-process monitoring
new modes of service delivery like cloud computing (including Saas, Paas, Iaas), and presentation layer extensions (e g. mobile computing allowing intensified interaction with the system in daily operations).
To transform an enterprise system into a process innovation platform, organizations must have a comprehensive understanding of new technologies
SAP€ s latest design thinking and business process transformation initiatives) 5 Summary Enterprise systems research has become a mature area in the field of information systems in the last years.
Journal of Information technology, 19 (1), 4†20. Mell, P, . & Grance, T. 2011). The NIST definition of cloud computing (800†145.
Gaithersburg: National Institute of Standards and Technology. Nambisan, S. 2013. Information technology and product/service innovation:
A brief assessment and some suggestions for future research. Journal of the Association for Information systems, 14 (4), 215†226.
Nandhakumar, J.,Rossi, M, . & Talvinen, J. 2005). The dynamics of contextual forces of ERP implementation.
Journal of Strategic Information systems, 14 (2), 221†242. Salleh, S m.,Teoh, S. Y, . & Chan, C. 2012).
Exploring new dimensions of information technology innovations. Journal of the Association for Information systems, 10 (1), 1†30.
Weick, K. E. 1977. Organization design: Organizations as self-designing systems. Organizational Dynamics, 6 (2), 31†46.84 B. Schenk Process Innovation with Disruptive Technology in Auto Insurance:
Lessons Learned from a Smartphone-Based Insurance Telematics Initiative Jens Ohlsson, Peter Haâ ndel, Shengnan Han,
and Richard Welch Abstract Insurance telematics or usage-based insurance (UBI) is a potential game-changer for the insurance industry,
In this chapter, we demonstrate the insurer†s process innovation with smartphone-based insurance telematics, using the example of the â€oeif Safedrive†campaign
J. Ohlsson(*)•S. Han Department of Computer and Systems sciences, Stockholm University, Borgarfjordsgatan 12,16407 Kista, Sweden e-mail:
BPM †Driving Innovation in a Digital World, Management for Professionals, DOI 10.1007/978-3-319-14430-6 7 85 1 Introduction The smart cellular phone,
or smartphone, has become a ubiquitous personal device influencing a large portion of the contemporary individual†s daily life.
The capabilities of smartphones exhibit a dramatic increase compared to traditional feature phones due to (1) the user friendly human-machine interface design;(
2) the high processing power utilizing multi-core processor architecture and increased memory capabilities, and (3) the sensing capabilities.
Contemporary smartphones are equipped with a large set of sensors which sense the surrounding environment, including means for positioning via e g.,
, used for the detection of the orientation of the smartphone for automatic rotation of the displayed information;
Sensor fusion technologies include the combination of data streams from several different sensors into sought for information,
which enhance the calculated position, direction and movement of the bearer of the smartphone. By combining measurements from sensors with complementary properties, information with enhanced properties can typically be extracted.
Sensor-equipped measurement platforms with processing capabilities existed prior to the introduction of the smartphone,
but the smartphone made it a ubiquitous device available in large volumes and distributed to a large portion of the population†a fact that opens up opportunities for developing a range of disruptive technologies.
The sensing capabilities of the smartphone create exciting new application areas (Lane et al. 2010). ) Connecting millions or even billions of smartphones into large scale sensing systems enable time or location-based services in environment monitoring, intelligent transportation systems, applications in health and support for the ageing populations,
to mention only a few. Sheng, Tang, Xiao, and Xue (2013) list two paradigms for sensing via large-scale smartphone-based measurement systems, namely,(1) participatory sensing and (2) opportunistic sensing, where the former is based on an active
participation on the part of the smartphone owner and the latter has automated sensing without the interaction of the end-user.
The evolution of smartphone technologies together with its social and technical capabilities creates a solid foundation for innovating business processes in various industries.
An innovation is defined as â€oenew to the state of the art, †which basically means without known precedent (Abrahamson, 1996;
In this chapter, we present a case study in participatory sensing, namely insurance telematics, in which a smartphone-based Usage Based Insurance (UBI) product for a personalised car insurance is realized.
It is believed that the findings are of a general interest as an example of a disruptive technology,
Insurance Telematics Auto insurance is in most cases required by law to cover bodily injuries, property damage liability, for personal injury protection, and the like.
Thanks to the development within sensor technology and infrastructure for wireless communication, new premium programs have appeared,
or Insurance Telematics programs. Insurance telematics refers to the technology of sending, receiving, and storing information from and to road vehicles for insurances purposes (Bruneteau, 2012).
The market of UBI is expected to take off in some regions, leading to a penetration of up to a 40%share of total policies in 2020.
Currently, the market penetration is low, with the Progressive Casualty Insurance company in the US as the market leader with around 1. 4 million customers in their program (Insurance Telematics, 2012.
The program produced strong intellectual properties for understanding user driving behaviours (Desyllas & Sako, 2013.
Forecasts for the United kingdom are that 60%of the insured vehicles in 2020 will run under an insurance telematics program (Insurance Telematics, 2013.
like a smartphone (Fig. 1, pictures from left to right). An insurer can access actual driving behaviour data through an insurance telematics program.
As a result, the insurance premium can be adjusted individually Process Innovation with Disruptive Technology in Auto Insurance 87 based on driving behaviour,
Insurance telematics has helped insurers to use other variables to improve their risk assessment and price calculations.
By using telematics technology, the insurers can improve the pricing accuracy and sophistication, as well as attract favourable risks.
The possibility of obtaining a scalable technology for insurance telematics has increased the insurance companies†interest in smartphone-based programs
also thanks to the smartphones†high penetration, the development talent within the telecom industry, and the ease of deployment by using the regular means for distribution of mobile applications like Appstore or Google Play.
Fig. 1 Progressive insurance snapshot measurement probe for the onboard diagnostics (left; sensing device for the cigarette lighter outlet by Movelo (middle;
and smartphone with insurance telematics software from Movelo (right) 88 J. Ohlsson et al. 2. 1 The Smartphone-Based Insurance Telematics Application At the Department of Signal Processing, KTH
With the progress of the cellular phone from a low-functionality feature phone to versatile software-configurable sensing platform,
a new smartphone-based measurement probe is developed and subsequently deployed for commercial purpose (Haâ ndel, Ohlsson, Ohlsson, Skog, & Nygren, 2014).
The clear advantages using the smartphone in this context include its high availability, competitive price-performance metric,
and recognition by the users. The measurement probe may be fixed a installation in the vehicle, semi-fixed installation using the power and data outlets,
or a smartphone, as illustrated in Fig. 1. The probe monitors and transmits risk-related information to the insurers such as the speeding,
cornering, braking and accelerating habits, time and date, and road conditions. The information collected by the measurement probe can be used by the insurers to improve their risk assessment, and thus
through use of this data a particular driver†s behaviour can be assessed. 2. 2 The Vendor Movelo†s Motivation to Commercialize the Application In late 2009,
the idea of using the UBI in an innovative business model was initiated. A legal entity was founded and formed outside of academia (Movelo AB,
and smartphone development in combination with the research activities at the universities were a catalyst for the moving vehicle logger campaign that was set up by Movelo AB and If P & C in early 2013.
enabled by the novel insurance telematics technology, such as a smartphone solution, the insurers need innovated marketing
and sales processes to facilitate and get a maximum effect out of the new product.
and sales process by getting a new customer channel and improved customer relations through the new possibility of communicating to their customers via the smartphone;(
An insurance telematics initiative, If Safedrive, was tested commercially. The insurance telematics initiative set up the following goals:
•Create a unique solution and mobile application that attracts car-drivers, especially new customers, based on the core-technology;
•Increase sales volumes; •Improve knowledge regarding car drivers/customer risk-behaviour; •Improve risk-assessment activities;
The insurer If P & C applied the new insurance telematics. Moreover, enabled by the insurance telematics solution,
the firm innovated their sales and marketing process, introducing the new UBI product to consumers.
Because of the insurance telematics solution new capabilities for risk assessment was enabled, such as driving-behaviour. The aim was to capture driving behaviour as early as possible in the marketing and sales process,
The process innovation and redesign work was done by means of an iterative approach Movelo prototyped the smartphone solution
The vendor tested it with different invited user groups and continuously refined the usecases of the solution.
Fig. 2 Examples of the smartphone interface and feedback to car-drivers from top to down/left to right:
The purpose of the commercial release was to implement the smartphone application in real driving scenarios with larger group customers/car drivers.
if the smartphone-based UBI fulfilled the initiative objectives, e g.,, creating sales-volumes and acquiring new customers. 3. 1 The Process Innovation:
Customer Acquisition Process The application of the smartphone-based UBI telematics transformed the insurer If†s sales
e g. the end user has bought a new car or that the end user is at the end of his/her policy period
and is actively searching for a better insurance product. The end user can call the insurer,
get an outbound call from the insurer, request a quotation via the insurer web-site, or visit an insurance broker to get quotes from several insurers.
and the price calculation is done based on risk-criteria data i e. age, number of years with a driver†s license
and type of car that the end user wants to insure. The next activity is to send the quote to the end user;
if the end-user accepts the conditions in the quotation, then an invoice is sent. When the invoice is paid,
the insurer has few interaction points with the end user and each interaction point has related costs, e g. call-centre costs, human cost,
or web-channel costs. 3. 1. 2 The To-Be Customer Acquisition Process Already in the start events,
the To-Be process (Fig. 4) differs from the As-Is by engaging end users (Car Drivers) in a novel way.
and implemented with the aims of taking maximum advantage of the new customer channel (the Smartphone) and its communication capabilities.
This was done to enable the end users (car drivers) to invite other end users whom they considered to be safe and ecological drivers,
Therefore, more end users joined the campaign and met the challenges of qualifying for the UBI car-insurance product.
price C ar-D riv er Car-Driver Download app Received invitation to safe-driving challenge
or curious of testing safe-drivning qualification Qualify for Safe-driving scores Communicate Quote via Smartphone Accept Quote Pay invoice Customer insured Challenge one
If Safedrive application to want to download it, test it, and then start a qualification for safe-driving scores,
and then qualifying for the new UBI insurance telematics product. The next phase in the To-Be process was the qualification activity:
In this activity of the To-Be process the end user received feedback from his/her smartphone on driving behaviour after each drive (see Fig. 2). The feedback consisted of scores 0†100 based on braking, acceleration and speeding behaviour,
for usability reasons, these parameters were presented not to the end user in the feedback and scoring.
Another feature of the solution that was shown not to end users was the Movelo real time feedback interface,
If real-time feedback is to be presented to end users, the smartphone should be mounted on the dash board,
thus, a cradle to put the smartphone in should be fixed in the car, which results in extra costs for end users.
Thus the feedback during the driving was passive, only showing that the solution was indicated running,
as by a spinning wheel (Fig. 2). After the qualification activity the car driver got an aggregated score
Usage grade is calculated by the application by comparing the odometer data (mileage) with the mileage recorded in the application.
The end user took a picture of his/her odometer when starting the qualification, and made a new picture when finishing the qualification.
with the distances that the user had the If Safedrive application running while driving. In the smartphone solution one can set up an auto-start function,
thus the end users can have the application start by itself when driving. Driving behaviour parameters were measured by advanced signal-processing algorithms,
filtering GPS data combined with sensor fusion from the accelerometer and gyroscope in the smartphone,
and combined with map-data in the smartphone (Haâ ndel et al.,2014). ) The complementary parameters for the risk-assessment process
and price calculation regarding exposure were time of day, road type and distance driven. These parameters were given high weight in the price calculation.
These exposure parameters have a high effect on risk assessment. For example, a driver who drives in rush hours on a road type with a high frequency of collisions
more and more end users will adopt the new insurance product based on their driving behaviours, i e. insurance telematics.
The advantages can be summarised as:(1) the To-Be process would save costs with regard to contact end users
and communicate with drivers; the insurer has more touch points with consumers in the process;(
and (3) the price calculation for each customer is based on the dynamic measurements instead of static statistics. 3. 2 The Results of the Insurance Telematics Initiative The
In total, some 1, 000 registered users were involved in the test. The pilot generated in total big data containing 4, 500 driving hours and 250,000 km road vehicle traffic data (Haâ ndel et al.
2014). ) The campaign was one of the first campaigns, worldwide, utilizing the processing power of smartphones.
The data quality was assured by rigorous soft computing methods. However, the results did not fulfil all the initiative goals.
The insurer If P & C decided to put the trial on hold. The insurer delayed the roll out of the new insurance product to mainstream customers.
000 signed users in the first 48 h when the campaign was launched, and large majority of the users recommended the smartphone application to friends,
it failed to recruit the desired amount of new customers. Most of the users were already customers of If P & C. However,
the If Safedrive application created much attention among end users. During the first 48 h after the application was released on Appstore,
the If Safedrive application was ranked at number 8 of the downloaded applications within its category in Sweden.
Table 1 The advantages of the To-Be customer acquisition process Process/sub-process As-Is (static) To-Be (dynamic) To-Be process advantages Customer acquisition Consumer makes insurance request through †Internet †Call centre †Broker
Or Insurer makes an outbound call to recruit new customer Consumer makes insurance request through †Smartphone App New customers recruitments are made by word-of-mouth, e g. inviting friends, social communities
since the interactions occur every time the users drive. Risk assessment Age, postal code, number of years of driving, gender, car type, previous insurance records/claims Driving behaviour (breaking, acceleration, speeding
road type, distance driven) The rich driving data help predict driving risks, and the loss costs for highest risk driving behaviour.
Price calculation Based on the static demographic data and historical statistics Based on the dynamic changes of driving behaviour (UBI) Customers get an accurate and personalized price.
The core technology of the smartphone-driven insurance telematics has yielded the advantage of improving risk assessments activities by collecting
and violating privacy issues in implementing the telematics to analyse end user driving behaviours. For instance, they criticized that â€oethe insurance industry†s hunger to chart customers in real-time may prove larger than Facebook and Google†(Computersweden, 2013.
This criticism also impacted the firm†s decision to halt the campaign. We were not able to evaluate the system capacity for providing forecasts of traffic flow,
Discussion In the case of the If Safedrive campaign, the smartphone-based insurance telematics was tested
Once the end users qualified as safe drivers, they were offered a new insurance product based on their driving behaviours,
Therefore, the process innovation with disruptive technology such as insurance telematics can†t be achieved and sustained at this moment.
The CLM consists of seven layers, from the core inner layer, encompassing technology (innovation/disruptive technology design),
information (data generated by the disruptive technology), business process design for the core technology implementation, product/services Process Innovation with Disruptive Technology in Auto Insurance 97 implementation, individual organization readiness for innovation implementation, towards business models and the outer
layer of business strategy. Business environment is conceptualized as the macro economic and market environment that a company operated within.
where the insurer applied smartphone-based insurance telematics to innovating business processes, i e. customer acquisition, risk assessment and price calculation.
A possible solution to this dilemma could be to unbundle the business by separating an insurance telematics initiative to another division and brand (Osterwalder & Pigneur, 2010.
Due to the unique context and core subject of this case study, the generalization of the results may be limited.
Why insurance telematics matters†Overview of a future-EUR 50 billion market. Paper presented at the Telematics Munich, Munich, Germany.
Collins, J. C. 2005. Built to last: Successful habits of visionary companies. London: Random House.
Smartphone-based measurement systems for road vehicle traffic monitoring and usage-based insurance. IEEE Systems Journal, 8 (4), 1238†1248. doi:
Insurance Telematics. 2012). ) Global study†Free abstract. Brussels: Ptolemus Consulting Group. Retrieved March 31, 2014, from www. ptolemus. com Insurance Telematics.
2013). ) Usage based insurance global study†Free abstract. Brussels: Ptolemus Consulting Group. Retrieved March 26, 2014, from www. ptolemus. com Kimberly, J. R,
A survey of mobile phone sensing. IEEE Communications Magazine, 48, 140†150. Malcolm, G. 2000. The tipping point:
www. progressive. com/Content/pdf/..//snap shot report final 070812. pdf. Last Accessed 8 january, 2015. Rosemann, M, . & vom Brocke, J. 2010).
The six core elements of business process management. In J. vom Brocke & M. Rosemann (Eds.
Handbook on business process management 1 (pp. 107†122. Berlin: Springer. 100 J. Ohlsson et al.
Process Innovation with Disruptive Technology in Auto Insurance 101 Part III Driving Innovation Through Advanced Process Analytics Extracting Event Data from Databases to Unleash Process Mining Wil M
and to guide users towards â€oebetter†processes. Dozens (if not hundreds) of process-mining techniques are available
, BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly.
This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion,
The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes
binds, and classifies data to create â€oeflat†event logs that can be analyzed using traditional process-mining techniques.
W. M. P. van der Aalst(*)Architecture of Information systems, Eindhoven University of Technology, P o box 513,5600 MB Eindhoven, The netherlands International Laboratory of Process-Aware Information systems
of event data is rapidly changing the Business Process Management (BPM) discipline (Aalst, 2013a; Aalst & Stahl, 2011;
model-based analysis and model-based implementation without using the valuable information hidden in information systems (Aalst, 2011).
and only organizations that intelligently use the vast amounts of data available will survive (Aalst, 2014).
Today†s main innovations are intelligently exploiting the sudden availability of event data. Out of the blue, â€oebig Data†has become a topic in board-level discussions.
The abundance of data will change many jobs across all industries. Just like computer science emerged as a new discipline from mathematics
when computers became abundantly available, we now see the birth of data science as a new discipline driven by the torrents of data available in our increasingly digitalized world. 1 The demand for data scientists is rapidly increasing.
However, the focus on data analysis should not obscure process-orientation. In the end good processes are more important than information systems and data analysis.
The old phrase â€oeit†s the process stupid†is still valid. Hence, we advocate the need for process scientists that will drive process innovations
while exploiting the Internet of Events (Ioe). The Ioe is composed of: •The Internet of Content (Ioc:
all information created by humans to increase knowledge on particular subjects. The Ioc includes traditional web pages, articles, encyclopedia like Wikipedia, Youtube, e-books, newsfeeds, etc. •The Internet of People (Iop:
all data related to social interaction. The Iop includes e-mail, facebook, twitter, forums, Linkedin, etc. •The Internet of things (Iot:
all physical objects connected to the network. The Iot includes all things that have a unique id and a presence in an internet-like structure.
Things may have an internet connection or be tagged using Radio-Frequency Identification (RFID), Near Field Communication (NFC), etc. •The Internet of Locations (Iol):
refers to all data that have a spatial dimension. With the uptake of mobile devices (e g.,
, smartphones) more and more events have geospatial attributes. Note that the Ioc, the Iop, the Iot, and the Iol partially overlap.
For example, a place name on a webpage or the location from which a tweet was sent. See also Foursquare as a mixture of the Iop and the Iol.
It is not sufficient to just collect event data. The challenge is to exploit it for process improvements.
Process mining is a new discipline aiming to address this challenge. Process-mining techniques form the toolbox of tomorrow†s process 1we use the term â€oedigitalize†to emphasize the transformational character of digitized data. 106 W. M. P. van der Aalst scientist.
Process mining connects process models and data analytics. It can be used: •to automatically discover processes without any modeling (not just the control-flow,
but also other perspectives such as the data-flow, work distribution, etc.),•to find bottlenecks and understand the factors causing these bottlenecks,
•to detect and understand deviations, to measure their severity and to assess the overall level of compliance,
•to predict costs, risks, and delays, •to recommend actions to avoid inefficiencies, and •to support redesign (e g.,
, in combination with simulation. Today, there are many mature process-mining techniques that can be used directly in everyday practice (Aalst, 2011.
The uptake of process mining is illustrated not only by the growing number of papers and plug-ins of the open source tool Prom, there are also a growing number of commercial analysis tools providing process mining capabilities, cf.
Disco (Fluxicon), Perceptive Process Mining (Perceptive Software, before Futura Reflect and BPMONE by Pallas athena), ARIS Process Performance Manager (SOFTWARE AG), Celonis Process Mining (Celonis Gmbh
), Processanalyzer (QPR), Interstage Process Discovery (Fujitsu), Discovery Analyst (Stereologic), and XMANALYZER (XMPRO. Despite the abundance of powerful process-mining techniques and success stories in a variety of application domains, 2 a limiting factor is the preparation of event data.
The Internet of Events (Ioe) mentioned earlier provides a wealth of data. However, these data are a not in a form that can be analyzed easily,
and need to be extracted, refined, filtered, and converted to event logs first. The starting point for process mining is an event log.
Each event in such a log refers to an activity (i e.,, a well-defined step in some process) and is related to a particular case (i e.
a process instance. The events belonging to a case are ordered and can be seen as one â€oerun†of the process.
Event logs may store additional information about events. In fact, whenever possible, process-mining techniques use extra information such as the resource (i e.,
or data elements recorded with the event (e g.,, the size of an order. If a BPM system or some other process-aware information system is used,
then it is trivial to get event logs, i e.,, typically the audit trail provided by the system can directly be used as input for process mining.
However, in most organizations one encounters information systems built on top of database technology. The Ioe depends on a variety of databases (classical relational DBMSS or new â€oenosql†technologies.
Therefore, we provide a database view on event data and assume that events leave footprints by changing the underlying database.
Fortunately, database 2 For example, http://www. win. tue. nl/ieeetfpm/doku. php? idâ shared: process mining case stud ies lists over 20 successful case studies in industry.
Extracting Event Data from Databases to Unleash Process Mining 107 technology often provides so called â€oeredo logs†that can be used to reconstruct the history of database updates.
This is what we would like to exploit systematically. Although the underlying databases are loaded with data, there are no explicit references to events, cases, and activities.
Instead, there are tables containing records and these tables are connected through key relationships. Hence, the challenge is to convert tables and records into event logs.
Obviously, this cannot be done in an automated manner. To understand why process-mining techniques need â€oeflat event logs†(i e.
Therefore, we need to relate raw event data to process instances using a single well-defined view on the process.
we focus on the problem of extracting â€oeflat event logs†from databases. First, we introduce process mining in a somewhat more detailed form (Sect. 2). Section 3 presents twelve guidelines for logging.
this paper aims to exploit the events hidden in existing databases. We use database-centric view on processes:
the state of a process is reflected by the database content. Hence, events are merely changes of the database.
In the remainder we assume that data is stored in a database management system and that we can see all updates of the underlying database.
This assumption is realistic (see e g. the redo logs of Oracle. However, how to systematically approach the problem of converting database updates into event logs?
Section 4 introduces class and object models as a basis to reason about the problem. In Sect. 5 we show that class models can be extended with a so-called event model.
The event model is used to capture changes of the underlying database. Section 6 describes a three-step approach (Scope, Bind,
and Classify) to create a collection of flat event logs. The results serve as input for conventional process-mining techniques.
Section 7 discusses related work and Sect. 8 concludes this paper. 2 Process Mining Process mining aims to discover,
monitor and improve real processes by extracting knowledge from event logs readily available in today†s information systems (Aalst,
or data elements recorded with the event (e g.,, the size of an order. Table 1 shows a small fragment of a larger event log.
2013b) for more information on the data possibly available in event logs. Flat event logs such as the one shown in Table 1 can be used to conduct four types of process mining (Aalst, 2011.
Extracting Event Data from Databases to Unleash Process Mining 109 The Prom framework provides an open source process-mining infrastructure.
Instead, we focus on the event data used for process mining. 3 Guidelines for Logging The focus of this paper is on the input side of process mining:
event data. Often we need to work with the event logs that happen to be available,
There can be various problems related to the structure and quality of data (Aalst, 2011; Jagadeesh Chandra Bose, Mans, & Aalst, 2013.
Before we present our database-centric approach, we introduce twelve guidelines for logging. These guidelines make no assumptions on the underlying technology used to record event data.
In this section, we use a rather loose definition of event data: events simply refer to â€oethings that happenâ€
and that they are described by references and attributes. References have a reference name and an identifier that refers to some object (person, case, ticket, machine, room, etc.)
and analyzing event data. Different stakeholders should interpret event data in the same way. GL2:
There should be structured a and managed collection of reference and variable names. Ideally, names are grouped hierarchically (like a taxonomy or ontology.
c) perceptive process mining (Perceptive Software;(d) Celonis process mining (Celonis Gmbh)( Color figure online) Extracting Event Data from Databases to Unleash Process Mining 111 specific extensions (see for example the extension mechanism of XES (IEEE Task force
on Process Mining, 2013b. GL3: References should be stable (e g.,, identifiers should not be reused or rely on the context).
, usage of data. GL7: If possible, also store transactional information about the event (start, complete,
Having start and complete events allows for the computation of activity durations. It is recommended to store activity references to be able to relate events belonging to the same activity instance.
Event data should be as â€oeraw†as possible. GL11: Do not remove events and ensure provenance.
For example, do not remove a student from the database after he dropped out since this may lead to misleading analysis results.
Sensitive or private data should be removed as early as possible (i e.,, before analysis). However, if possible, one should avoid removing correlations.
They can be used to better instrument software After these general guidelines, we now change our viewpoint.
We aim to exploit the hidden event data already present in databases. The content of the database can be seen as the current state of one or more processes.
Updates of the database are considered therefore as the primary events. This database-centric view on event logs is orthogonal to the above guidelines. 4 Class
and Object models Most information systems do not record events explicitly. Only process-aware information systems (e g.,, BPM/WFM systems) record event data in the format shown in Table 1. To create an event log
we often need to gather data from different data sources where events exist only implicitly.
In fact, for most process-mining projects event data need to be extracted from conventional databases. This is often done in an ad hoc manner.
Tools such as XESAME (Verbeek, Buijs, Van dongen, & Aalst, 2010) and Promimport (Guâ nther & Aalst, 2006) provide some support,
but still the event logs need to be constructed by querying the database and converting database records (row in tables) into events.
Moreover, the â€oeregular tables†in a database only provide the current state of the information system.
It may be impossible to see when a record was created or updated. Moreover deleted records are generally invisible. 3 Taking the viewpoint that the database reflects the current state of one or more processes,
we define all changes of the database to be events. Below we conceptualize this viewpoint.
Building upon standard class and object models, we define the notion of an event model. The event model relates coherent set of changes to the underlying database to events used for process mining.
Section 5 defines the notion of an event model. To formalize event models, we first introduce
and define class and object models. A class model defines a set of classes that may be connected through relationships.
UML class models (OMG 2009), Entity-Relationship (ER) models (Chen, 1976), Object-Role Modeling (ORM) models, etc. provide concrete notations for the basic class model used in this paper. 3 Increasingly systems mark deleted objects
In this way all intermediate states of the database can be reconstructed. Moreover, marking objects as deleted instead of completely removing them from the database is often more natural, e g.,
, concerts are not deleted†they are canceled, employees are not deleted†they are fired, etc. Extracting Event Data from Databases to Unleash Process Mining 113 Definition 1 (Unconstrained Class Model) Assume V to be some universe of values (strings
numbers, etc..An unconstrained class model is a tuple UCM Â C; A r; val; key;
there cannot be two concerts on the same day in the same concert hall Fig. 2 Example of a constrained class model (Color figure online) Extracting Event Data from Databases to Unleash Process Mining 115
and class models in a database. However, it is easy to map any class model onto a set of related tables in a conventional relational database system.
but it is obvious that the conceptualization agrees with standard database technology. 5 Events and Their Effect on the Object model Examples of widely used Database management systems (DBMSS) are Oracle RDBMS (Oracle), SQL SERVER (Microsoft), DB2 (IBM), Sybase (SAP),
and Postgresql (Postgresql Global Development Group). All of these systems can store and manage the data structure described in Definition 4. Moreover,
all of these systems have facilities to record changes to the database. For example, in the Oracle RDBMS environment, redo logs comprise files in a proprietary format 116 W. M. P. van der Aalst
which log a history of all changes made to the database. Oracle Logminer, a utility provided by Oracle,
provides methods of querying logged changes made to an Oracle database. Every Microsoft SQL SERVER database has a transaction log that records all database modifications.
Sybase IQ also provides a transaction log. Such redo/transaction logs can be used to recover from a system failure.
The redo/transaction logs will grow significantly if there are frequent changes to the database. In such cases, the redo/transaction logs need to be truncated regularly.
This paper does not focus on a particular DBMS. However, we assume that through redo/transaction logs we can monitor changes to the database.
In particular, we assume that we can see when a record is inserted, updated, or deleted. Conceptually, we assume that we can see the creation of objects
and relations (denoted byï¿),), the deletion of objects and relations (denoted byï¿),), and updates of objects (denoted by ï¿).
) Based on this we define the set of atomic and composite event types. Definition 5 (Event Types) Let CM Â C;
Extracting Event Data from Databases to Unleash Process Mining 117 Definition 6 (Events) Let CM Â C;
If the customer is already in the database, the composite event cannot contain the creation of the customer object c6.
model (Color figure online) Extracting Event Data from Databases to Unleash Process Mining 119 Next we define the effect of an event occurrence, i e.,
This is denoted by OM0) L OMN. 120 W. M. P. van der Aalst The formalizations above provide operational semantics for an abstract database system that processes a sequence of events.
However, the goal is not to model a database system. Instead, we aim to relate database updates to event logs that can be used for process mining.
Subsequently, we assume that we can witness a change log L Â e1; e2;..enh i. It is easy to see atomic events.
and/or user id). Definition 3 shows that this assumption allows us to reconstruct the state of the database system after each event, i e.,
Table 1 shows the kind of input data that process-mining techniques expect. Such a conventional flat event log is a collection of events where each event has the following properties:
the person, machine or software component executing the event. †Type: the transaction type of the event (start,
Dedicated process-mining formats like XES or MXML allow for the storage of such event data.
one may convert it into a conventional event by Extracting Event Data from Databases to Unleash Process Mining 121 taking tsi as timestamp and eni as activity.
1) scope the event data,(2) bind the events to process instances (i e.,, cases), and (3) classify the process instances. 6. 1 Scope:
Determine the Relevant Events The first step in converting a change log into a collection of conventional events logs is to scope the event data.
One way to scope the event data is to consider a subset of event namesens ï¿
The notion of process instances is made explicit in process-aware information systems, e g.,, Business Process Management (BPM) and Workflow Management (Wfm) systems.
Process cubes are inspired by the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice,
However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values.
and classify†approach allows for the transformation of database updates into events populating process cubes that can be used for a variety of process-mining analyses. 7 Related Work The reader is referred to (Aalst, 2011) for an introduction
Next to the automated discovery of the underlying process based on raw Extracting Event Data from Databases to Unleash Process Mining 123 event data,
and to guide users towards â€oebetter†processes. Dozens (if not hundreds) of process-mining techniques are available
but about getting the event data needed for all of these techniques. We are not aware of any work systematically transforming database updates into event logs.
Probably, there are process-mining case-studies using redo/transaction logs from database management systems like Oracle RDBMS, Microsoft SQL SERVER, IBM DB2,
or Sybase IQ. However, systematic tool support seems to be missing. The binding step in our approach is related to topic of event correlation
which has been investigated in the (web) services (Aalst, 2013c). ) In Aalst, Mooij, Stahl, and Wolf (2009) and Barros, Decker, Dumas,
and Weber (2007) various interaction and correlation patterns are described. In Pauw et al. 2005) a technique is presented for correlating messages with the goal to visualize the execution of web services.
Also Montahari-Nezhad, Saint-paul, Casati, & Benatallah (2011) developed techniques for event correlation and process discovery from web service interaction logs.
Most closely related seem to be the work on artifact-centric process mining (ACSI, 2013; Fahland, Leoni, Dongen, & Aalst, 2011a;
but cannot be applied easily to selections of the Internet of Events (Ioe) where data is distributed heterogeneous
Process mining seeks the â€oeconfronta-tion†between real event data and process models (automatically discovered or handmade).
The 15 case studies listed on the web page of the IEEE Task force on 124 W. M. P. van der Aalst Process Mining (IEEE Task force on Process Mining,
and filtering the event data. The twelve guidelines for logging presented in this paper show that the input-side of process mining deserves much more attention.
database systems. This paper focused on supporting the systematic extraction of event data from database systems.
Regular tables in a database provide a view of the actual state of the information system.
For process mining, however, it is interesting to know when a record was created, updated, or deleted.
Taking the viewpoint that the database reflects the current state of one or more processes,
we define all changes of the database to be events. In this paper, we conceptualized this viewpoint.
The event model relates changes to the underlying database to events used for process mining.
A logical next step is to develop tool support for specific database management systems. Moreover, we would like to relate this to our work on process cubes (Aalst, 2013b) for comparative process mining.
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A further subject will be how innovations can be converted from confidence-based to evidence-based models due to affordances of digital infrastructures such as large-scale enterprise software or social media.
innovation is seen also as applicable to the development of new service offerings, new business models, new processes or new management J. Recker(*)Information systems School, Queensland University of Technology, 2
Thus, the physical IPHONE was the obvious attractor, but it was the App store that created an innovative and novel business model that provided a separate ongoing value proposition and added income stream.
While innovation is linked not necessarily to information technology, it is established well that successful technology innovation can lead to new businesses,
innovative information technology solutions drive organizational change (Markus & Robey, 1988. In fact, new products and services can be sufficiently successful to create entirely new markets (Berry, Shankar, Parish, Cadwallader, & Dotzel, 2006.
or even Youtube videos) to convey messages such as â€oewe should be doing this tooâ€. Cases†or videos and stories of cases†can provide only limited evidence,
Moving to reliable, valid and ultimately credible decisions about innovations through evidence-based decision-making requires an ability to work with data
available and quality data that can be used as evidence. They also require a capability to collect
analyze and interpret such data to prepare for decisions. Table 2 summarizes relevant requirements. These scientific capabilities can obviously be provided by universities and research institutions.
and interpret data using rigorous scientific methods, research can provide additional innovation support services: •Novel conceptual perspectives:
Evidence that is converted from data gathered and analyzed scientifically can provide a solid and trustworthy platform for decision-making about innovations, their potential, pitfalls and consequences. •Increased research bandwidth:
Table 2 Requirements for evidence-based innovation decisions Capability Requirements Data awareness Identifying appropriate data Finding available data Understanding the quality of Data science
and gather objective data that can be used as facts in innovation decisions. I use the term digital infrastructure in a deliberately loose manner,
as an umbrella term to capture all sorts of Information technology platforms†those that exist to facilitate
share and collaborate (think of social networking or social media) as well as those that exist specifically to create and assist process management
and innovation efforts (such as BPM engines, modeling tools or those that allow for open innovation, idea exchange or collaborative design).
While some of these technologies, such as enterprise system software, have been around for decades, recent years have seen also a rapid uptake of modern digital infrastructures that transcend the business-private life boundary, such as social networking platforms,
or complement historical transaction data with real-time data and analytics, such as in-memory technology. These digital infrastructures provide ample opportunities for evidence-based management in process innovation.
Some of their affordances include: •Footprinting: all actions, decisions and processes carried out on digital infrastructures leave a trace.
or indeed about the innovation processes themselves. •Crowdsourcing: Most digital infrastructures provide platforms that connect a multitude of users who are geographically and temporally dispersed.
This means that every problem that is normally confined to a particular place and time†in the digital world†can be offered to others outside the team,
Modern digital infrastructures often provide not only facts about behaviors on these platforms or access to other resources and users,
but typically also advantages in analytics and computing power; that is, while more data can be generated,
more can also be analyzed and used. A classical example is that of Google analytics that offers free analysis of web browsing behavior, ready at the fingertips of any decision-maker.
A more recent trend is in-memory technology, 136 J. Recker which provides affordances to process
and analyze large volumes of data in real-time (vom Brocke, Debortoli, Muâ ller, & Reuter, 2014).
Traditionally, fact finding in support of decision-making†in the context of BPM methodologies such as Six Sigma and others†has always been hampered by sheer pragmatic concerns about the feasibility, resourcing and costing of data collection efforts.
Data that is generated on digital platforms is located typically at the other end of the scale:
Data points are generated well beyond the sample size required to reach conclusive findings about the data.
It is no longer acceptable not to peruse available data and evidence in making process-related decisions.
this is a research challenge where data such as store size, quality of baking, number of competitors in the market, customer demographics,
Having examined these factors by studying technology data (such as point-of-sales, HR and payroll systems, census data about customer demographics) as well as empirical data from studying the stores and process participants themselves,
conclusions can be made about the occurrence of positive deviance. In a nutshell, in our example the findings were as follows:
in that creative staff were finding new solutions for products, display and service, and sometimes†where appro-priate†willingly deviated from the standardized process.
who use data from an information system, together with their detailed knowledge of local customers, local events and all other factors that will influence sales.
One key technological innovation in replenishment processes has been the emergence of a specific kind of information system, namely, automated inventory replenishment systems,
one notices that 140 J. Recker making an informed decision about the potential process improvement is a significant data analysis challenge.
Combining evidence from past sales data, forecasting algorithms as well as observations and evidence from how store managers operate,
volume Low volume System MAPE System MAE Trend line MAPE Trend line MAE M AE Fig. 4 Data analytics in the replenishment process 2 In replenishment,
data scientists are becoming an essential resource in developing a capability to identify, understand, analyze and interpret evidence in support of innovation decisions about business processes.
Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90 (10), 70†76.
Crowdsourcing systems on the worldwide web. Communications of the ACM, 54 (4), 86†96. Dreiling, A,
Paper presented at the 17th Pacific Asia Conference on Information systems, Jeju Island, Korea. Edmondson, A c. 2011.
Journal of Strategic Information systems, 18 (1), 46†55. Malsbender, A.,Recker, J.,Kohlborn, T.,Beverungen, D,
Tracing the progress of innovations borne on enterprise social network sites. Paper presented at the 34th International Conference on Information systems, Milan, Italy.
Markus, M. L, . & Robey, D. 1988). Information technology and organizational change: Causal structure in theory and research.
Management Science, 34 (5), 583†598. Nagji, B, . & Tuff, G. 2012). Managing your innovation portfolio.
Scientific research in information systems: A beginner†s guide. Berlin: Springer. Rosenberg, W, . & Donald, A. 1995).
Communications of the Association for Information systems, 34 (7), 151†168. Evidence-Based Business Process Management:
Deviance mining deals with the analysis of process execution logs offline in order to identify typical deviant executions
M. Dumas(*)•F. M. Maggi Institute of Computer science, University of Tartu, J. Liivi 2, Tartu 50409, Estonia e-mail:
what data to gather and when and how to make decisions. A number of approaches for flexible process management have emerged in recent years.
and to collect different subsets of data at different points in the process, with as few restrictions as possible.
and tell the user what is the impact of a given action on the probability that the case at hand will fail to fulfill the relevant performance objectives or compliance rules.
and Yang (2013) in the context of software defect handling processes in a large commercial bank in China.
600 defect reports of 4 large software development projects and examined the differences between reports that had led to a correct resolution (normal cases)
versus those that had led to complaints by users (anomalous cases). The team defined a number of features to distinguish between normal and anomalous complaints,
In a second step, data is collected with respect to the chosen performance measures. This is followed (third and fourth steps) by identifying samples of exceptional performance from the data
and analyzing the data in an exploratory manner in order to identify what factors might underpin the identified exceptional performance (positive deviance).
In a fifth step, statistical tests are used to identify correlations and causal links between the identified factors and positive deviance.
these techniques provide a basis to monitor ongoing executions of a process (a k a. cases) in order to assess whether they comply with the constraints in question.
in that they allow users to identify a violation only after it has occurred rather than supporting them in preventing such violations in the first place.
what input data values to provide, so that the likelihood of violation of business constraints is minimized. 150 M. Dumas and F. M. Maggi In this paradigm,
a user specifies a business goal in the form of business rules. 2 Based on an analysis of execution traces,
the idea of predictive monitoring is to continuously provide the user with estimations of the likelihood of achieving each business goal for a given case.
and (2) the values of data attributes after each activity execution in a case. As an example, consider a doctor who needs to choose the most appropriate therapy for a patient.
Historical data referring to patients with similar characteristics can be used to predict what therapy will be the most effective one
given contextual information about the application and the personal data of the applicant (e g.,, age, salary, etc..
2013), we have put forward a specific framework for predictive monitoring aimed at generating predictions at runtime based on user-defined business goals.
and for every data input that can be given to this activity, the probability that the execution of the activity with the corresponding data input will lead to the fulfillment of the business goal.
To this aim, we apply a combination of simple string matching techniques with decision tree learning. An approach for the prediction of abnormal terminations of business processes has been presented by Kang
Here, a fault detection algorithm (local outlier factor) is used to estimate the probability of a fault occurring.
and Pontieri (2012) propose a predictive clustering approach in which context-related execution scenarios are discovered
and Outlook Process innovation requires business process support systems that depart from traditional normative approaches to business process execution.
and more user friendly tool support that allows them to readily apply these techniques on potentially large and complex business process execution logs.
Acknowledgments This work is supported by ERDF via the Estonian Centre of Excellence in Computer science. References Birukou
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In Proceedings of the SIAM international conference on data mining (SDM)( pp. 644†655. SIAM. 154 M. Dumas and F. M. Maggi Identification of Business Process Models in a Digital World Peter Loos, Peter Fettke, Juâ rgen Walter, Tom Thaler,
process experiences can be the core input for process model designs using a more innovative bottom up approach with inductive methods,
P. Loos(*)•P. Fettke •J. Walter •T. Thaler •P. Ardalani German Research center for Artificial intelligence (DFKI), Saarland University
3) variation of the modelling level, e g. software reference model or (4) variation of the modelling purpose,
The research approach of this work stands in the tradition of German design science oriented research in the modelling of enterprise information systems (Frank, 2006:
while Sect. 4 introduces a software tool†the Refmod-Miner†realizing these and more techniques.
Finally, Sect. 6 closes the article with a conclusion and an outlook on future work.
in particular, different techniques, software tools and application scenarios are described in greater detail in Sects. 3†5. 2 Related Work Several authors describe a procedure model for reference modelling
and do not consider the modelling of business information systems in general. Furthermore, some approaches utilize an inductive strategy (Aier, Fichter, & Fischer, 2011;
Interviews with domain experts or potential model users can give guidance concerning the requirements that the reference model should fulfil. •Literature review:
•Clustering: In a clustering step the different individual models are grouped in a way such that models within one group are similar
and models belonging to different groups are different. Here, typical techniques of cluster analysis or multivariate statistics can be used.
The modelsynset created in phase 3 can support the grouping. Known similarity measures for enterprise models can also be applied (Dijkman et al.
Instead, these have to be negotiated in a discourse between the model developers, the users and the evaluators.
and can comprise database schemata (e g. Evermann, 2009) as well as arbitrary other model schemata. Process matching can be divided into two different fields†matching process models (1) and matching nodes of process models (2)( Thaler, Hake, Fettke, & Loos, 2014.
However, most of the existing techniques and algorithms only take activities into account. There are several different approaches for the automatic detection of correspondences.
so that the algorithm is able to determine whether a node should be an activity or an event (in case of EPCS).
The Refmod-Mine/NSCM algorithm conducts an n-ary cluster matching, thus, the nodes of all models which should be matched are being compared pairwise,
& Flynn, 1999) cluster algorithms start with clusters of size 1 (activities) and consolidates two activities to a cluster
whereby stop words and waste characters like additional spaces are removed and (2) computing the Porter Stem stem wilã°Ã (Porter,
which decides on the similarity being 0 or sim (L1, L2). In the end, the Refmod-Miner/NSCM technique extracts binary matchings from the calculated node clusters.
Finally, the algorithm returns binary simple or complex matches for the nodes of each model pair. 4. 2 Structural Analogies One of the main problems in reference modelling is the identification of correspondences (cf. 4. 1)
In order to match (cf. 4. 1) further elements, it is necessary to use advanced mapping algorithms that are able to identify antonyms like â€oeinvoice settled†and â€oepayment receivedâ€.
In comparison to the linear time computation of an RPST (Vanhatalo et al. 2009), the calculation of subgraph isomorphism is said to be NP complete (Garey & Johnson, 1979.
The MCC algorithm comprises three main steps: In the first step, a set of candidaterelations is calculated out of the existing nodes and edges in given Fig. 2 Structural analogue process chains Identification of Business Process Models in a Digital World
the algorithm is also able to present a completely integrated model containing all nodes of the underlying process models
an example is shown in Fig. 3. Three sample EPCS in a model variant collection represent the input data.
a corresponding software tool was developed. The goal of the tool development was not to support a fully automated development of a reference model.
JAVA was used as the programming language. The architecture of the tool consists of three layers that are shown in Fig. 4. At the lowest layer, functionalities for loading, storing,
conversion and transformation as well as versioning of model data are available. Generally, two file formats are supported:
the ARIS Markup language (AML) and EPC Markup language (EPML. The second layer contains concepts and algorithms
which support the analysis of individual enterprise models and the derivation of a reference model.
In a first step, clustering techniques are used to identify and reconstruct the given model groups. Since the model repository consists of 80 single models with 8 different processes and 10 variants each,
(3) creating a homogeneous data basis for different application and analysis scenarios. Moreover, the authors aim at publishing the model corpus in terms of open models,
much as in the open source idea in the context of software development. However, this very much depends on the license holder of the model corpus†content.
/It contains 98 reference model entries with lexical data and meta-data, such as the number of contained single models.
•an application of the inductive method to develop new reference models and •the development and application of techniques and algorithms for the corpus development.
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Hence, BPM projects are conducted in large, possibly interorganizational environments (Houy, Fettke, J. Becker(*)Department of Information systems, University of Muenster, Leonardo-Campus 3, 48149 Muâ nster, Germany e-mail:
modeling software must be built such that the rules are impossible to violate. Adherence to semantic standardization rules
, data view or organizational view) are modeled, it is important to maintain a systematic relationship between modeling elements from different views to ensure all models are integrated properly.
They propose using algorithms to parse labels written by modelers in order to detect grammatical mistakes and to prompt users to fix them. 3. 2 Use a Glossary As naming conventions only refer to standardizing the grammatical part,
the logical next step is to also standardize the meaning of terms. To do so, Reijers et al.
The simplest solution would be to not allow users to type in textual descriptions into activities
This solution is most transparent to the user and provides him with an overview of existing terms,
when using techniques from computational linguistics. A user could be allowed to type in natural language text, as he would do
if there were no controlled vocabulary. An algorithm can then parse his sentence and deconstruct it into its parts.
This allows not only a verification of the phrase structure, i e.,, whether it is compliant with the desired naming convention such as verb-object style.
as has been demonstrated by a prototypical software artifact (Delfmann, Herwig, & Lis, 2009). The most challenging task,
Not only can this help to create more clearly arranged models (provided that the algorithm is good),
These conventions should be codified in the form of a layout algorithm which is the same for each modeler.
Otherwise, there is a risk that readers misinterpret certain aspects of some models. 4 The Icebricks Approach The icebricks tool is a process modeling software prototype developed to support BPM consulting projects in the retail industry.
Its process models can be used for process-oriented reorganization, software selection, and similar project objectives.
which could be anything that can be stored as a file on a computer (MS OFFICE documents, pictures, videos, BPMN diagrams, etc.).
& Shitkova (2013). 5 Conclusion and Outlook In this chapter, I have argued for rigorously standardizing process models by embedding modeling conventions into digital modeling tools.
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Weber, B.,Reichert, M.,Mendling, J, . & Reijers, H. A. 2011). Refactoring large process model repositories.
Computers in Industry, 62 (5), 467†486. Weidlich, M.,Dijkman, R, . & Mendling, J. 2010).
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Actors within the telecommunication sector forecast 50 billion connected devices just within a couple of years. An increased connectivity for enhanced collaboration could be met by digital innovations that bring new values and opportunities for existing and new actor participation in the ecosystem see also chapter by Schmiedel and vom Brocke (2015.
The empirical data was derived from a series of workshops and interviews with the key stakeholders along the process steps
Up to now there are no approaches on how to measure, monitor, and evaluate ecosystem-wide performance. Coming to an understanding of how to conceive ecosystem is one essential step towards the development of such approaches.
At the core of this traditional view, defined by Goldkuhl and Lind (2008) as â€oebusiness process as sequential transformationâ€,
fulfilling, and evaluating expectations are made core issues, thereby constituting structures for actions. Expectations are covered by the assignment in
Due to an increased degree of digital connectedness and increased flow of data from assets and actions within the ecosystem, there are great possibilities to ensure that the value production becomes even more coordinated
and collaboration between engaged actors through its ability to enable actors to share desired data.
the focus is put upon the door-to-door traveller†s process with three core processes (to, within,
Some parking lots can be booked pre via the airport website. When the car has been parked the passenger needs to get to the right terminal
or finds out which terminal he/she is expected to depart from. 3. 4 At the Core of the Door-to-door Process:
For other processes such activities are seen rather to occur between the different core processes. Since these activities are outside the project scope
and energy efficient flows (as means for KPA#3). Each of the (digital) innovations initiated in the future Airport project had the goal to contribute to these values (see Fig. 4 below) founded on patterns of data streams as the common information environment.
there is a lack of approaches to measure, monitor, and evaluate the performance of ecosystems covering co-production of value by multiple actors.
and performance metrics, allowing correct measurement data to be obtained and for the results to be interpreted based on relevant contextual factors (explanatory factors),
what data should be replicated in a management dashboard. 3. 8 Innovation 2: Information Sharing Platforms for Situational Awareness:
The ambition with a management dashboard is to enable digital images providing status of the D2d process for key stakeholders with relevant data in real time for the purpose of increased punctuality and customer satisfaction.
and turn-around 208 M. Lind and S. Haraldson core of this innovation is a tool for providing digital images (see Fig. 6 above) based on information from different key actors,
which better describes the fluid manner of interaction between developers and users of information services.
and small third party developers design the latest traveller support services using commonly available data. There are a number of novel insights to be made.
Second, consumers of digital services (e g. the travellers) are also suppliers of feedback data, encompassing feedback on digital services, new ideas on digital services, the use of physical infrastructures and transport
which data should be provided. Fig. 7 Example of a passenger dashboard channelized via different media 210 M. Lind
including similar systems that have airports as their core connection point, for ecosystems within the transport domain,
. & Roâ stlinger, A. 2002), Towards an integral understanding of organisations and information systems: Convergence of three theories.
In Australian conference on information systems, Australia. Haraldson, S, . & Lind, M. 2011b). Dividing multi-organizational businesses into processes:
In Australian conference on information systems, Australia. Iansiti, M, . & Levien, M. 2004). The keystone advantage †What the new dynamics of business ecosystems mean for strategy, innovation, and sustainability.
Organizational semiotics †Evolving a science of information systems (pp. 211†230. IFIP TC8/WG8. 1. Kluwer.
Understanding computers and cognition: A new foundation for design. Norwood, NJ: Ablex. (Air) port Innovations as Ecosystem Innovations 213 Leveraging Innovation Based on Effective Process Map Design:
This value chain describes a process view of an organization that builds on a set of core processes.
The core processes are those that create value for the customer, such as sales, logistics, etc.
but required for conducting core processes. These are called support processes (e g. HR, finance, etc..Explicit interdependencies between both these processes can be identified.
which was found on the website ariscommunity. com. It shows an abstract process-oriented representation of an insurance company.
Core processes Management processes Supporting processes Manage compliancy Manage audits Manage governance Financial reporting Fig. 2 Process map example found at http://www. ariscommunity. com
Organizations commonly differentiate between core processes, support processes and management processes. In addition, there are some companies that keep the analysis
where a core process is decomposed into smaller part-processes, so-called sub-processes (Malinova & Mendling, 2013).
When a core process starts, the set of its corresponding sub-processes need to be executed in order for the core process to finish.
Another commonly observed primary notation concept is the sequential relation between the core processes. This can be observed from the close proximity of two core processes,
when these stand next to each other. For this, many maps use Porter†s value chain concept (Malinova & Mendling, 2013;
As a result, all core processes are represented in such a way that there is a temporal sequential order between them.
when each core process is executed. Moreover, when process maps that represent the core processes in a value chain manner,
they also indicate the input of the value chain and its consequent output. The input is usually a customer request,
For example, core processes are represented either by a rectangle-shaped symbol, which obviously indicates a singular process,
pointing to the direction of the core process category. This direction (position and orientation) also infers some meaning,
in which the support processes apparently are there to support the core processes, while the management processes manage the execution of the core processes.
This way, a relation also between the processes that belong to different categories can be depicted implicitly.
Moreover, the close proximity of two core processes typically implies a sequential order between these processes.
For example, an input pointing only to the core process category means that this particular input will trigger the execution of one or a set of core processes.
clusters are defined for core, support and management processes. Beyond this, the map also indicates a notion of process containment within the core process Fig. 3 The original process map of a European insurance company Leveraging Innovation Based on Effective Process Map Design:
Insights from...223 category. This we infer from the labels used for each sub-process.
For example, the processes shown in the core process category win customers and control sales are clearly a subset of the core process sales.
In addition, based on the order of the four core processes (sales, actuarial, administration and capital allocation) and the inputs and outputs coming from and to some of the core processes (sealed contracts, fees,
estimates) we could infer certain sequence of execution. This company used few visual variables to complement the primary notation.
On the other hand, the light brown color differentiates between core processes and their corresponding sub-processes. The management and support processes are using the rectangle-shaped symbol,
while the core processes and sub-processes use a pentagon-shaped symbol positioned horizontally. This might indicate that these processes include activities that are done in a particular order.
The input/output notions we found to be primary concepts are complemented by an arrow between the core processes.
indeed, there is a sequential order between the core processes. The explicitly labeled inputs and outputs, together with the directed arrow, clearly depict what a core process needs to produce in order to finish,
thus starting the execution of the next in line process. 4. 3 Discussion of the Original Process Map For a process map to be cognitively effective it needs to reflect the principles of visual notations by Moody (2009).
Despite the fact that the symbols used for the core processes differ from the rest, the color remains the same.
There is a notion of process order between the core processes. However, it is difficult, only by looking at the process map,
On the other hand, we can observe the use of additional text to indicate the output of one core process and the input of another (e g. sealed contracts.
While this significantly assists in 224 M. Malinova and J. Mendling understanding the nature of the respective core processes,
this input/output text is shown not between all core processes (e g. actuarial). As such, it will potentially lead to misinterpretations and misunderstandings among the end-users.
Now, management as well as support processes are visualized with symbols different to the ones of the core processes.
Second, the core business has been subdivided into three segments which are interrelated. The customer-facing processes partially interact with the recipient-facing processes.
This was also the main reason for the missing input between the actuarial and administrative core processes from Fig. 3. The original process map provided an inappropriate representation of the interfaces
which were in fact necessary for the relation between the two core processes. In this way, innovation at these crossroads was blocked by the fact that it was captured simply not in an explicit manner.
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C a. L. Oliveira(*)•R. M. F. Lima Center for Informatics Federal University of Pernambuco, Av.
rmfl@cin. ufpe. br H. A. Reijers Department of mathematics and Computer science, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The netherlands e-mail:
most organizations cannot exploit the opportunities given by information technology to solve these issues see also chapter by Schmiedel and vom Brocke (2015).
An architecture for the implementation of this concept in information systems is presented, as well as a prototype system that we have constructed
) The alignment between business processes and strategic goals has been approached by information systems literature in different ways.
Then, the system collects and monitors such metrics during the execution of the process. Due to the popularity of the BSC methodology among executives, many packaged solutions offer standard business process models that are mapped already into BSC metrics (Brignall & Ballantine, 2003.
) They empirically tested these argu-ments through a survey with IT service providers and software development companies.
) Unfortunately, current information systems do not offer support towards making the users aware of their role in the success of the organization†s strategy.
We define strategy awareness as an information system†s capacity to influence users to work towards the strategic priorities of the organization.
, to build a strategy model that is not only stored in an information system but that can also be interpreted by a computer to extract useful information from it.
To clearly express the elements that make up a strategy and how they relate to each other,
We call a strategic recommendation any information that helps the user take decisions and perform activities in align-ment with the strategic priorities of the organization.
a strategic recommendation would be advice to the user telling him whether he should or should not proceed with a path that involves such expenses.
Decision-making algorithms can be used to compile and present to the user recommendations about how to perform their job in alignment with the organ-ization†s priorities.
Then, the output of their work can be monitored and compared to the recommended work products
and strategic recommendations are compiled to inform the user about the strategic requirements of his work.
we apply concepts drawn from the architectures of context-aware information systems. In general, these systems are composed of three main elements:(
1) the user application services;(2) a context acquisition and reasoning module; and (3) an adaptation mechanism.
and provide services to the user in a way that is optimized to the current context of use. On the basis of these concepts, we design four main modules that compose an SA-BPM system†s architecture:(
and interacts with the user. The other modules communicate with the BPM engine to 236 C a. L. Oliveira et al. implement the strategy-related features.
It is also responsible for obtaining data about the performance indicators, their historical data and current values,
such as, strategic recommendations that should be presented to the user during the execution of that instance of the process. 3. 2. 2 Strategic Adapters The Strategic Adapters
(or just adapters) are pieces of software that affect the execution of the process to include new activities, alternative paths,
A third adapter may give the user a recommendation to avoid printing a document when a digital copy is available
The adapters created in this way may communicate with the Context Provider through web services to acquire information from the context
and decide which information should be shown to the user. The advantage of this approach is that the company can build adapters using the same knowledge that is used to design a regular business process. 3. 2. 3 Strategic Adaptation Agent The Adaptation Agent is the module responsible for monitoring the execution
To show the strategic recommendations to the user, the Adaptation Agent also affects the user interface of the BPM system to enrich the user†s experience.
All recommendations stored in the context of the process instance are made available to the user,
so that he can become aware of them and perform the activities in accordance. The information shown to the user includes also the connections between the work products generated by the activity
and all elements of the results-chain that are affected, their corresponding performance indicators and targets,
the Context Provider is a web service that stores the results-chain and that uses business intelligence mechanisms to extract data from performance indicators.
Both regular business processes and adapters are modeled using BPMN notation and deployed to the engine.
The Adaptation Agent is a stand-alone application that monitors the process execution in the Bonita BPM engine,
All variables from the process instance are copied to the adapters so all adapters can have access to the process data.
On the basis of this data, the adapters can compute recommendations and store them in the Context Provider.
these activities are communicated to the user, as usual in any regular business process. As soon as all adapters have concluded their execution
During this process, the Agent changes the user interface so that the user can become aware of the execution of these adapters
and of the strategic recommendations computed by them. The Bonita BPM€ s interface enriched by ROSAS with strategic recommendations is illustrated in Fig. 3. This picture shows a typical input form shown by Bonita BPM during the execution of a process.
Over this form a sliding panel is added by ROSAS where recommendations specifically targeted for the activity being executed are shown.
When the users execute the process, the visual feedback given by ROSAS helps them recognize changes in the Fig. 3 Bonita BPM interface enriched by ROSAS to display strategic recommendations Implementing a Digital Strategy through Business Process Management 239 priorities
of the organization and understand how these changes impact their own job. 5 Application Scenario To demonstrate the application of SA-BPM concepts and tools,
If the user is not aware of the trade-offs involved and the objectives of the organization,
it requests the user to estimate the freight tariffs for external carriers. As soon as the employee has gathered this information and input into the system
a decision algorithm is employed automatically to determine the recommended path to be followed by the user.
Truck assigned to shipment schedule Text shown to the user: â€oeplease, avoid contracting an external carrier for these packages,
External carrier contracted to perform shipment Text shown to the user: â€oeplease, contract an external carrier to deliver these orders,
thus, are affected by the decision that the user is going to make at this moment. Through the SA-BPM system, the user becomes aware of the relevance of the activity he is executing and of
what he should do to contribute to the success of the strategy. Strategy Success Improved competitiveness in the cost leadership segment Increased market presence Improved operational efficiency Improved distribution agility and efficiency Reduced delivery cost
Notice that, in this case, the user needs to balance the trade-offs between cost reduction and financial stability
Should the user prefer one to the other? What is more important for the company? In SA-BPM, all information necessary to help the user in taking a decision that affects the strategic performance of the company must be made accessible to him.
In the situation of a conflict between recommendations, the system must rank the recommendations according to the company†s priorities at the time.
the recommendations can be shown to the user together with their ranks. So, the user can know that, for instance,
the financial stability of the company is in a critical situation and that this much more important than reducing the cost of deliveryâ€
Expert systems with Applications, 37 (4), 3274†3283. Kaplan, R. S, . & Norton, D. P. 1992).
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and to use information systems (IS) to support these. In particular, enterprise resource planning systems (ERP) or workflow management systems (Wfms) have been implemented
Since validation is the core of compliance (Cannon & Byers, 2006) and it is usually a â€oemanual†task requiring noteworthy time and effort,
Further methods and tools like the so-called (rule-based) monitors or ex post auditing approaches, e g.,
, cyber physical systems, big data analysis, business intelligence approaches, or process mining provide more and more results in real-time.
, data elements, values, organizational units, or temporal characteristics. Finally, similar to classical â€oeby design†Flexible Workflow Management System (WFMS) Adapted Workflow Instance D es ig n-tim e R un
Then, using all these pieces of information it becomes possible to identify points for integrating concrete control activities into workflows. This can be realized by having automated search algorithms check the actual control parameters against the workflow instance information.
On the server side, the Logmanager is extended for intercepting execution events (see Fig. 2, where white/blue fields are the original components of Aristaflow,
which are captured by user â€oemeyer13†must be checked. Thus, in a second step, a compliance officer has to define a corresponding reference control.
the execution/server side was chosen as an ideal place for integrating the extending elements of Kitcom.
Aristaflow BPM Server Process Repository Control conditions Kitcom Plug-in Event Analysis Control Processes Control Injection Control Process Repository Reference Control Editing
Fig. 6 Kitcom architecture extending Aristaflow BPM server Start capture invoice 1 approve account approved?
yes pay invoice claim invoice End no Fig. 7 Usual workflow execution with the software Aristaflow Flexible Workflows and Compliance:
if the user â€oemeyer13†captures an invoice with an amount over 5, 000 â, the reference control will automatically be integrated as a sub-process called â€oecontrol†shown in Fig. 8,
Current technological progress and the ongoing trends to analyze business data quasi in real-time will,
-organizational business processes, integrating emerging technologies or social media, real-time adaptation to changing workflows to execution context, advanced process analytics results,
Data Knowledge Engineering, 53, 129†162. Betke, H.,Kittel, K, . & Sackmann, S. 2013). Modeling controls for compliance †An analysis of business process modeling languages.
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Data and Knowledge Engineering, 50, 9†34. Sackmann, S. 2011. Economics of controls. In Proceedings of the international workshop on information systems for social innovation 2011 (ISSI 2011)( pp. 230†236.
Tachikawa, Tokio. Sackmann, S.,Hofmann, M, . & Kuâ hnel, S. 2013). Return on controls invest †Ein Ansatz zur wirtschaftlichen Spezifizierung von internen Kontrollsystemen.
When information technology (IT) is used to enable business innovations, one also speaks of digital innovations (SAP, 2013).
and then focus on social media as new technologies. 2. 1 IT Supports Drastic Process Improvements Business process reengineering (BPR) is the ultimate domain of drastic process improvements to create IT-enabled end-to-end processes.
an organization consists of a web of interacting processes and people (like in a supply chain, a business network or for outsourcing).
IT, especially the Internet, is seen now as the most important enabler to connect the world in a seamless web of transactions.
and •process-aware information systems (e g. a BPM suite) to allow business people to model,
deploy and optimize business processes themselves (i e. without manual programming by software engineers). This third BPM wave initiated by Smith
which is created if software architectures and application development methods pose technical constraints to the execution of BPM.
, also through new technologies like social media. Nowadays, social media have gained in importance. Not only do millions of people
(or customers) have an account with one or more of the social media tools like Twitter,
Facebook, Linkedin, Google+,Youtube, Pinterest, etc. but many organizations have jumped also on the social bandwagon,
and try to create value from social media. Since social media make use of Web 2. 0 as a technological platform,
they can be seen as the next step in the Internet evolution (Dachisgroup, 2012; Woodcock, Green, & Starkey, 2011.
Social media use within an organization requires a multi-disciplinary approach, which means that it is limited not to marketing or IT departments.
Social Customer Relationship Management (social CRM) is the ultimate key domain to illustrate how social media can affect new and existing business processes.
Social CRM is: a philosophy and a business strategy, supported by a technology platform, business rules, workflow, processes and social characteristics, designed to engage the customer in a collaborative conversation
in order to provide mutually beneficial value in a trusted and transparent business environment (Greenberg, 2009). As such, social CRM means truly listening to customers,
It is driven user in order to turn fans and followers into customers and even advocates of a brand.
) •Regarding existing business processes, feedback or complaints received by means of social media can give insightful input towards adjusting an organization†s way of working (i e. business rules and operations.
and found out that already 88%monitor customer feedback and conversations on social media platforms and 64%of respondents collect online feedback and also turn them into process improvements or product improvements.
Social media can also stimulate internal collaboration, for instance by internal networks like Yammer, resulting in a better customer service delivery. •Social media can also facilitate people†s involvement from idea generation to the realization of new products and services,
and thus new business processes. Particularly, forums, communities, contests and polls can stimulate customer collaboration
Similarly, the computer company Dell has a community for crowdsourcing ideas called Ideastorm (http://www. ideastorm. 262 A. Van Looy com/).
Another similarity is based on the fact that all previous key domains describe IT (e g. software
and social media) as an enabler for (redesigning business processes. Particularly the first two key domains still present business processes as being dominated by engineering.
Consequently, information systems should provide business-specific functionality, which typically requires knowledge of business processes. As in the previous section, the interrelationship is discussed first for IT development.
•Zachman†s enterprise architecture framework (1987) categorizes different artifacts of organizational data that are required for IT development, e g. design documents, specifications, and models.
1) what (data),(2) how (function or process),(3) where (network),(4) who (people),(5) when (time),
2014) 268 A. Van Looy information system), entailing a clear link to digital innovations. Some nontechnical examples for these capabilities may include, among others:(
Skype. Also the ability to interpret and use the technical process output metrics may facilitate the mentioned strategic alignment,
and can therefore build a bridge between IT and corporate goals (e g. by process-aware information systems).
or knowledge sharing databases. 4. 1 Learnings The process capability framework and the underlying maturity models illustrate that BPM can be approached from a technical perspective
based on information found on the website http://www. bpmroundtable. eu, /the cultural research conducted in Liechtenstein is situated mainly in the upper layer of the framework (with organizational characteristics that impact the whole process portfolio),
Community relationship management and social media. Database Marketing and Customer Strategy Management, 18 (1), 31†38.
Basu, S. C, . & Palvia, P. C. 2000). Business process reengineering. In A. Kent (Ed.),Encyclopedia of library and information science (Vol. 67, pp. 24†34.
In Proceedings of the 18th Australasian conference on information systems (pp. 642†653. Toowoomba, Australia. Forrester.
Retrieved from http://c. ymcdn. com/sites/www. simnet. org/resource/group/62bde4a1-974a-4105-BE98-BA41ED782AA3/presentations/makingitmatterinbusinessinno. pdf Forrester.
://i. dell. com/sites/doccontent/corporate/secure/en/Documents/listening -and-engaging-in-the-digital-mar keting-age. pdf Greenberg, P. 2009).
Time to put a stake in the ground on social CRM. Retrieved from http://the56group. typepad. com/pgreenblog/2009/07/time-to-put-a-stake-in-the-ground-on-social-crm. html/Hammer, M. 2007.
The process audit. Harvard Business Review, 85 (4), 111†123. Hammer, M, . & Champy, J. 2003).
Version 1. 0. Retrieved from http://www. omg. org/spec/BPMM/1. 0/PDF Resch, M. 2011.
The six core elements of business process management. In J. vom Brocke & M. Rosemann (Eds.
Retrieved from http://global. sap. com/campaigns/2013 06 bt award/assets/BT%20award%20application. docx Schwalbe, K. 2010.
Information technology project management. Boston, MA: Course Technology, Cengage Learning. Schmiedel, T.,vom Brocke, J, . & Recker, J. 2013).
Software project secrets: Why software projects fail. Berkeley, CA: Apress. The Standish Group. 2013). ) CHAOS Manifesto 2013.
Retrieved from http://versionone. com/assets/img/files/Chaosmanifesto2013. pdf Van Looy, A.,De Backer, M,
. & Poels, G. 2011). Defining business process maturity. A journey towards excellence. Total Quality Management and Business Excellence, 22 (11), 1119†1137.
Enterprise Information systems, 8 (2), 188†224. Van Looy, A. 2014. Business process maturity. A comparative study on a sample of business process maturity models (Springerbriefs in business process management.
Database Marketing and Customer Strategy Management, 18 (1), 50†64. Zachman, J. A. 1987. A framework for information systems architecture.
IBM Systems Journal, 26 (3), 276†292.274 A. Van Looy Driving Process Innovation: The Application of a Role-Based Governance Model at Lufthansa Technik Janina Kettenbohrer, Mirko Kloppenburg,
and Daniel Beimborn Abstract Many stakeholders are involved in process operation and, consequently, also in process improvement and innovation.
J. Kettenbohrer(*)Department of Information systems and Services, University of Bamberg, An der Weberei 5, 96047 Bamberg, Germany e-mail:
which are a core part of a governance model, supports process-oriented decision-making and managing cross-functional processes more effectively (Braganza & Lambert, 2000;
Furthermore, Process Responsibility takes over ownership of processes, master data, and customized system settings. This includes the definition of process trainings and participation in the appointments of process management roles.
and the third maintains flight computers. In this example, each workshop would run a product-specific process instance of the process
Due to the clustering of process instances this person can work fulltime as a Process Manager and the profession-alism (i e.,
Spender & Kessler, 1995) by defining communication flows which are presented in Fig. 4. FAR+uses five core communication flows,
This specific process describes the creation and publishing of process documentations in a role-based, process-oriented software system.
process manager and resource responsible •No structured meeting •No fixed schedule Process operation •Quarterly video conference meeting •In advance to the process strategy Process
Paper presented at the 31st International Conference on Information systems (ICIS), St louis. Loshin, D. 2008. Master data management.
Burlington: Morgan Kaufmann. Markus, M. L, . & Jacobson, D d. 2010). Business process governance. In J. vom Brocke & M. Rosemann (Eds.
Prozessmanagement als Kernkompetenz (Process management as core competence)( 5 ed.).Wiesbaden: Gabler. Rosemann, M, . & De Bruin, T. 2005).
Paper presented at the 13th European Conference on Information systems, Regensburg, Germany. Sackmann, S, . & Kittel, K. 2015).
Curricula Vitae Wil van der Aalst Eindhoven University of Technology, The netherlands Wil van der Aalst is a Full professor of Information systems at the Technische Universiteit Eindhoven (TU/e). He is the Academic
Supervisor of the International Laboratory of Process-Aware Information systems of the National Research University in Moscow.
His work is cited highly (highest H-index among European computer scientists, 115 according to Google Scholar. In 2012, he received the doctor honoris causa from Hasselt University, Belgium.
, Germany Peyman Ardalani has been doing his academical research as a Ph d. student since 2012 at the Institute for Information systems (IWI) at the German Research Institute for Artificial intelligence (DFKI.
Earlier he has completed two Bachelor degrees in the fields of Software engineering and Economics pursuing his Master of science degree in the field of Information technology Engineering.
His research activities mainly focus on practical solutions for integrating business process models, analyzing the similarity of business process models and developing reference models.
In his earlier careers he has been involved in project management and designing software architecture and developing Content Management Systems (CMS) for more than 10 years.
Joâ rg Becker University of Muâ nster, Germany Joâ rg Becker is Full professor and head of the Department of Information systems at the University of Muâ nster.
He is the Academic Director of the European Research center for Information systems (ERCIS. He is Editor in Chief of the journal Information systems
and E-business Management and serves on various editorial boards. His work has appeared in several journals (e g.,
, Communications of the AIS, Information systems Journal, and Business Process Management Journal) and was presented on international conferences (e g.
Germany Daniel Beimborn is Full professor for Information systems at the Frankfurt School of Finance & Management, Germany.
such as MIS Quarterly, Journal of Management Information systems, Journal of IT, and he is member of the Editorial Review Board of the Journal of the AIS.
Marlon Dumas University of Tartu, Estonia Marlon Dumas is Professor of Software engineering at University of Tartu, Estonia.
a collaborative research center that gathers ten Estonian IT organizations with the aim of conducting industry-driven research in service engineering and data mining. From 2000 to 2007,
Curricula Vitae 289 Peter Fettke Saarland University, Germany Peter Fettke obtained a master†s degree in Information systems from the University of Muâ nster, Germany, a Ph d. Degree
in Information systems from the Johannes Gutenberg-University Mainz, Germany, and a Habilitation Degree in Information systems from the Saarland University, Germany.
Currently, he is the deputy chair of the Institute for Information systems (IWI) at the German Research center for Artificial intelligence (DFKI), Saarbruâ cken.
In 2013 he became a DFKI Research Fellow. Peter has taught and researched previously at the Technical University of Chemnitz and the University Mainz,
and Philosophy of Information systems. He uses both design-oriented and experimental research methods. Shengnan Han Stockholm University, Sweden Shengnan Han is a senior lecturer and associated professor at Stockholm University, Sweden.
Economics) in information systems at AË bo Akademi University, Finland in 2005. Since 2001, she started her research and practice in mobile services.
She worked in the large projects carried out with Duodecim (the Finnish Medical Society), Pfizer Finland Ltd, Nokia Ventures, Nokia Mobile phones, etc.
She is an expert on issues of user acceptance and evaluation of mobile services. Her research interests include social/mobile services, business process management, business-IT alignment, IT Governance, e-government,
His research interests include disruptive sensor based technologies like insurance telematics. Sandra Haraldson Viktoria Swedish ICT, Sweden Industrial researcher Sandra Haraldson is with the Sustainable Business group at Viktoria Swedish ICT.
She holds a Licentiate Degree in Information systems Development from Linkoâ ping University. She has a professional background from IT-consultancy related to different sectors.
and had a core role in applying BPM-related research leading to tangible results. Today she is engaged substantially in the EU-project MONALISA 2. 0 that has the goal to introduce a sea traffic management system for sustainable maritime transports.
She writes the popular â€oecolumn 2†BPM blog at www. column2. com and is featured a conference speaker on BPM.
Germany Janina is Graduate Research Assistant at University of Bamberg, Department of Information systems and Services.
She studied Information systems at University of Bamberg and she holds a Master of science. Her Ph d. topic covers the human side of business process standardization whereby her research focuses on business process standardization,
He has a background in business economics und computer science and has obtained his economic doctorate degree from the Martin-Luther-University Halle-Wittenberg in 2013.
Kai†s research interests are topics related to the use and management of information systems and information technology in business.
He holds a diploma in Business Information systems from the University of Cooperative Education Mannheim Germany, and a Master of business administration from University of Louisville, USA.
Curricula Vitae 293 Ricardo Massa F. Lima Federal University of Pernambuco, Brazil Ricardo Massa F. Lima received the Ph d. degree in computer science from Federal University
He is the Vice-Coordinator of UFPE€ s computer science postgraduate program. His main research interests include compiler construction and optimi-zation
and performance evaluation of discrete-event dynamic systems using Petri nets, with projects sponsored by the Brazilian Petroleum Industry (Petrobras), Saëoeo Francisco†s Hydroelectric Company (Chesf),
Dr. Lima is a member of the ACM and the Brazilian Computer Society. Mikael Lind Viktoria Swedish ICT and Chalmers University of Technology, Sweden Associate professor Mikael Lind is with the Viktoria Swedish ICT and Chalmers University of Technology.
He is also one of the initiators of Maritime Informatics for applied research of digitalization in the maritime sector. 294 Curricula Vitae Peter Loos Saarland University,
Germany Peter Loos is Director of the Institute for Information systems (IWI) at the German Research center for Artificial intelligence (DFKI)
and is Professor of Information systems at Saarland University. His research activities include business process management
information modelling, enterprise systems as well as implementation of information systems. Peter graduated from Saarland University (Dipl. -Kfm.).
Before he pursued a career in academics he worked for 6 years as software development manager.
Prior to this appointment, he was postdoctoral researcher in the Architecture of Information systems group at Eindhoven University of Technology.
declarative business process modeling and information systems monitoring. He has published close to 50 journal and conference articles in these fields.
He received a Ph d. in Computer science in 2010 from University of Bari. Curricula Vitae 295 Monika Malinova Vienna University of Economics and Business, Austria Monika Malinova is a teaching
She completed her Master studies in Information systems at the Humboldt Universitaâ t zu Berlin, Germany.
He has published more than 200 research papers, a o. in ACM Transactions on Software engineering and Methodology, IEEE Transaction on Software engineering, Information systems, Data & Knowledge Engineering,
Jens Ohlsson Stockholm University, Sweden Jens Ohlsson received the MSC in Computer and Systems sciences, Stockholm University, 1999.
Since 2011, he also holds a position at the Department of Computer and System Sciences
Ceâ'sar Augusto L. Oliveira University of Pernambuco, Brazil Ceâ'sar Augusto L. Oliveira received the M. Sc. degree in computer engineering from the Computing systems Department, University
of Pernambuco (Recife, Brazil) in 2008 and his Ph d. degree in computer science from the Center for Informatics, Federal University of Pernambuco (Recife, Brazil), in 2014.
He has worked also as a Consultant for the Inter-American Development Bank and the United nations Development Program on the subjects of strategic monitoring and business intelligence.
Curricula Vitae 297 Jan Recker Queensland University of Technology, Australia Jan Recker is the Woolworths Chair of Retail Innovation, Alexander-Von-humboldt Fellow and a Full professor for Information systems
Hajo Reijers Eindhoven University of Technology, The netherlands Hajo Reijers is a Full professor in Information systems at Eindhoven University of Technology as well as the head of BPM research at Perceptive Software.
He received a Ph d. in Computer science (2002), A m. Sc. in Computer science (1994), and A m. Sc. in Technology and Society (1994), all from TU/e. Hajo wrote his Ph d. thesis on the topic of BPM for the service industry
Australia Dr. Michael Rosemann is Professor and Head of the Information systems School at Queensland University of Technology,
he received a doctorate in 2003 and a professorship in information systems and business economics (Habilitation) in 2010.
Curricula Vitae 299 Bernd Schenk University of Liechtenstein, Liechtenstein Bernd Schenk is senior lecturer for Information systems at the University of Liechtenstein.
He holds a Ph d. in Information systems from the Vienna University of Economics and Business and a MSC from the University of Innsbruck, Austria.
His core research interests are Enterprise resource planning systems (ERP systems Serviceoriented Architectures (SOA), and Business Process Management (BPM).
Her research focuses on social aspects in information systems research particularly on the interconnection of culture and business process management (www. bpm-culture. org.
including Information & Management, Enterprise Information systems, and Business Process Management Journal, as well as in academic books and conference proceedings. 300 Curricula Vitae Tom Thaler Saarland University, Germany Tom Thaler is researcher at the Institute for Information systems (IWI) at the German Research center
for Artificial intelligence (DFKI) and research project lead at Saarland University. His research activities include business process management, process mining, software development as well as implementation of information systems.
After his study he worked as a Business intelligence Consultant at SAP. Since 2012 he coordinates the information systems study at Saarland University
and supervises several classes at Saarland University, Goâ ttingen University and VGU School of business Informatics. He is sponsored by the German Federal Ministry of Education and Research (BMBF) and currently works on his Ph d. thesis. Peter Trkman University of Ljubljana,
Slovenia Peter Trkman is Associate professor at the Faculty of Economics of the University of Ljubljana.
His research interests encompass business models and various aspects of business process, supply chain and operations management.
He participated in several projects and published over 70 papers including papers in journals like Decision Support systems,
IEEE Transactions on Engineering Management, International Journal of Information management, International Journal of Production Economics, Journal of Strategic Information systems, Long Range Planning and Wirtschaftsinformatik.
Her research is published in scientific outlets such as Total Quality Management & Business Excellence, Enterprise Information systems,
He is Professor of Information systems, the Hilti Chair of Business Process Management, and Director of the Institute of Information systems.
He is Founder and Co-Director of the international Master Program in â€oeit and Business Process Management†and Director of the Ph d. program in â€oeinformation and Process Management†at the University of Liechtenstein (www. bpm
including MIS Quarterly (MISQ), the Journal of Management Information systems (JMIS), and the Business Process Management Journal (BPMJ).
Walter studied computer science at the Brandenburg University of Technology (BTU. His research activities include business process management, software development and graph theory.
He reviewed several scientific project proposals as well as papers of established IS conferences like ICIS, ECIS, AMCIS, HICCS and EMISA and produced own publications,
100 Automatic layout, 183 B Big data, 3, 7, 10,22, 53,95, 106,250 Bottom up approach, 61 BPM.
See Capability layer model (CLM) Closed innovation, 5 Cloud, 12, 52†53,79, 80 computing, 13, 78†83 Co-creation, 34,39, 53â€
, 250†255,257, 267 Conventional decision-making, 133 Critical success factor, 70,83, 264,266, 271 Crowdsourcing, 136,262 Culture, 11,14, 23,56
Data awareness, 135 Database, 13,24, 32, 105†125,163, 165,271 Database management systems (DBMS), 107,108, 116,117, 119, 123†125 Data science, 106,135 DBMS.
See Database management systems (DBMS) Deployment models, 79 Design principle, 13,78, 98,135, 146,178, 179,182, 183,221 Deviance mining, 11,13, 146†153 Digital age, 4
, 257 Empirical evidence, 155 Enterprise software, 52,54 Enterprise system, 13,18, 21, 75†84,139, 297 Event data, 13, 105†125 Event log
, 20,21, 25 Information management, 135,300, 302 Innovation capabilities, 11†12,17 management, 300 space, 99 Insurance, 13, 85†100, 215†226 telematics
, 13, 85†100,291 Intelligent processes, 12,52 Internal and external stakeholders, 70 Internal controls, 248,249 Internet of events (Ioe
106,107, 124 Internet of things (Iot), 3, 7, 20,27, 106 Interpersonal communication, 60 J Job-to-be done (JTBD), 40,41, 45 K Kitcom, 248, 252â€
186,196, 206†209,211 Mobile technologies, 61 Modeling convention, 180†182,188 Modeling tools, 13,136, 177†189 Monitoring, 5, 9, 11,13, 22,55
/modeling, 13,60, 63†66,78, 108, 177†189,225, 263†268, 270†272,287 clustering, 161†162,170 collection, 170,216 corpus, 171 harmonization, 164
innovation, 41†44 Seven process modeling guidelines (7pmg), 181,183 Skills, 17, 53†56,131, 134,260, 267,269, 272 Smartphone, 7, 42, 85â€
100,106 Smartphone-based monitoring, 86 Smart processes, 23,25 Social BPM, 54†55 Social collaboration, 52†53,56 Social media, 3
7, 9, 12,13, 59†70,136, 257,260, 262,263 Social networks, 53,54, 60,61, 136 Stakeholder, 10, 59†65, 67†70,99, 111,131, 150,186, 195,196
270,302 T Technique, 13,36, 44,56, 106†111,121, 123,124, 146†153,157, 161†172,179, 184,200, 261,263, 271 Technological development, 5, 68,294 Telematics
Driving Innovation Through Emerging Technologies Emerging Technologies in BPM 1 Introduction 2 Emerging Technologies 2. 1 Mobile and Cloud 2. 2 Social Collaboration
and Distributed Co-creation 2. 3 Events, Big data and Analytics 3 The Changing Nature of Work 3. 1 Social BPM 3. 2 Dynamic Processes
3. 3 Cultural Changes 4 Smarter Processes 5 Summary References Leveraging Social media for Process Innovation.
Implications of Cloud computing 4 Openness of Enterprise Systems 5 Summary References Process Innovation with Disruptive Technology in Auto Insurance:
Lessons Learned from a Smartphone-Based Insurance Telematics...1 Introduction 2 The Disruptive Technology: Insurance Telematics 2. 1 The Smartphone-Based Insurance Telematics Application 2. 2 The Vendor Moveloâ's Motivation to Commercialize the Application 3 The Case of the If Safedrive Campaign 3
. 1 The Process Innovation: Customer Acquisition Process 3. 1. 1 The As-Is Customer Acquisition Process 3. 1. 2 The To-Be Customer Acquisition Process 3. 1. 3 The To-Be Process Advantages
3. 2 The Results of the Insurance Telematics Initiative 4 Lessons Learned: Discussion 5 Conclusions References Part III:
Driving Innovation Through Advanced Process Analytics Extracting Event Data from Databases to Unleash Process Mining 1 Introduction 2 Process Mining 3 Guidelines for Logging 4 Class
and Predictive Monitoring 1 Introduction 2 Business Process Monitoring Architecture 3 Deviance Mining 4 Predictive Monitoring 5 Discussion and Outlook References Identification
and Outlook References (Air) port Innovations as Ecosystem Innovations 1 Introduction 2 Multi-Actor Co-production in Ecosystems 2. 1 Conceiving Ecosystems and Their Innovation Needs
Getting to the Airport 3. 4 At the Core of the Door-to-door Process: Actions Performed Within Airports 3. 5 The Final Steps of the Door-to-door Process:
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