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Synopsis: Mining: Mining: Mining:

AI techniques may help in image mining. The alternative solution is to educate society with the aim of preventing crimes. 1. 2. 2. 2. Innovation at home The early development of home automation appeared in France in the 1980s.

Intelligent image mining systems can help to monitor what is happening. The exodus of people looking for jobs from village to town

observations, or more recently process mining. Subsequent activities are dedicated then to identifying process issues and their root causes

and Richard Welch Part III Driving Innovation Through Advanced Process Analytics Extracting Event Data from Databases to Unleash Process Mining...

129 Jan Recker Enabling Process Innovation via Deviance Mining and Predictive Monitoring...145 Marlon Dumas and Fabrizio Maria Maggi Identification of Business Process Models in a Digital World...

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

For example, monitoring and analyzing process performances based on digital processes enables real-time deviance mining, i e. the identification of best and worst process performances (see the chapters by Recker (2015) and by Dumas and Maggi (2015)).

He introduces an approach to create event logs from underlying databases as a fundamental prerequisite for the application of process-mining techniques

Marlon Dumas and Fabrizio Maria Maggi give insights on â€oeenabling Process Innovation via Deviance Mining and Predictive Monitoringâ€.

Enabling process innovation via deviance mining and predictive monitoring. In J. vom Brocke & T. Schmiedel (Eds.

Using process mining to learn from process changes in evolutionary systems. International Journal of Business Process Integration and Management, 3 (1), 61†78.

Many of the required process technologies and methods such as process mining and business analytics have been researched

and methods such as process mining and analytics have been researched and developed extensively (Grigori et al.,2004). ) Even business activity monitoring or complex event processing are available as off the shelves solutions (Luckham, 2011.

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

. P. van der Aalst Abstract Increasingly organizations are using process mining to understand the way that operational processes are executed.

Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks,

Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However,

binds, and classifies data to create â€oeflat†event logs that can be analyzed using traditional process-mining techniques.

Today, there are many mature process-mining techniques that can be used directly in everyday practice (Aalst, 2011.

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

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.

In fact, whenever possible, process-mining techniques use extra information such as the resource (i e.,, person or device) executing

, typically the audit trail provided by the system can directly be used as input for process mining.

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.

To understand why process-mining techniques need â€oeflat event logs†(i e. event logs with ordered events that explicitly refer to cases

First, we introduce process mining in a somewhat more detailed form (Sect. 2). Section 3 presents twelve guidelines for logging.

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,

2011). 108 W. M. P. van der Aalst Normally, â€oeflat†event logs serve as the starting point for process mining.

For example, many process-mining techniques use extra information such as the resource (i e.,, person or device) executing

We refer to the XES standard (IEEE Task force on Process Mining 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.

Process discovery is the most prominent process-mining technique. For many organizations it is surprising to see that existing techniques are indeed able to discover real processes merely based on example behaviors stored in event logs. •The second type of process mining is conformance.

this third type of process mining aims at changing or extending the a priori model. For instance, by using timestamps in the event log one can extend the model to show bottlenecks, service levels,

either discovered through process mining or (partly) made by hand, one can check, predict, or recommend activities for running cases in an online setting.

Extracting Event Data from Databases to Unleash Process Mining 109 The Prom framework provides an open source process-mining infrastructure.

Over the last decade hundreds of plug-ins have been developed covering the whole process-mining spectrum. Prom is intended for process-mining experts.

Non-experts may have difficulties using the tool due to its extensive functionality. Commercial process-mining tools such as Disco, Perceptive Process Mining, ARIS Process Performance Manager, Celonis Process Mining, QPR Processanalyzer, Fujitsu Interstage

Process Discovery, Stereologic Discovery Analyst, and XMANALYZER are typically easier to use because of their restricted functionality.

Figure 1 shows four screenshots of process-mining tools analyzing the same event Log in this paper,

we neither elaborate on the different process-mining techniques nor do we discuss specific process-mining tools.

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:

Such an event log can be used as input for a wealth of process-mining techniques.

The guidelines for logging (GL1€ GL12) aim to create a good starting point for process mining.

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).

For comparative process mining, it is vital that the same logging principles are used. If for some groups of cases, some events are recorded not

Reproducibility is key for process mining. For example, do not remove a student from the database after he dropped out since this may lead to misleading analysis results.

The main purpose of the guidelines is to point to problems related to the input of process mining.

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.

The event model relates coherent set of changes to the underlying database to events used for process mining.

Extracting Event Data from Databases to Unleash Process Mining 113 Definition 1 (Unconstrained Class Model) Assume V to be some universe of values (strings

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

Extracting Event Data from Databases to Unleash Process Mining 117 Definition 6 (Events) Let CM Â C;

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.,

Instead, we aim to relate database updates to event logs that can be used for process mining.

and Classify Process-mining techniques require as input a â€oeflat†event log and not a change log as described in Definition 10.

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:

Dedicated process-mining formats like XES or MXML allow for the storage of such event data.

To be able to use existing process-mining techniques we need to be able to extract flat event logs

into a collection of conventional events logs that serve as input for existing process-mining techniques.

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.

and compare the process-mining results. To allow for comparative process mining, process instances are classified using a relation class ï¿

PI ï¿CL with CL the set of classes. Consider for example the study process of students taking a particular course.

In (Aalst, 2013b), the notion of process cubes was proposed to allow for comparative process mining. In a process cube events are organized using different dimensions.

and drill-down process-mining results efficiently. As mentioned before, we deliberately remain at the conceptual level

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

to process mining. Alternatively, one can consult the Process Mining Manifesto (IEEE Task force on Process Mining, 2011) for best practices and the main challenges in process mining.

Next to the automated discovery of the underlying process based on raw Extracting Event Data from Databases to Unleash Process Mining 123 event data,

there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations,

Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. For example, dozens of process discovery (Aalst, 2011;

However, this paper is not about new process-mining techniques but about getting the event data needed for all of these techniques.

Probably, there are process-mining case-studies using redo/transaction logs from database management systems like Oracle RDBMS, Microsoft SQL SERVER, IBM DB2,

Most closely related seem to be the work on artifact-centric process mining (ACSI, 2013; Fahland, Leoni, Dongen, & Aalst, 2011a;

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,

2013a) illustrate the appli-cability of process mining. Process mining can be used to check conformance,

detect bottlenecks, and suggest process improvements. However, the most time-consuming part of process mining is not the actual analysis. Most time is spent on locating,

For process mining, however, it is interesting to know when a record was created, updated, or deleted.

The event model relates changes to the underlying database to events used for process mining.

bind, and classify†approach that creates a collection of event logs that can be used for comparative process mining.

Moreover, we would like to relate this to our work on process cubes (Aalst, 2013b) for comparative process mining.

Process mining: Discovery, conformance and enhancement of business processes. Berlin: Springer. Aalst, W. van der (2013a.

Slicing, dicing, rolling up and drilling down event data for process mining. In M. Song, M. Wynn,

Service mining: Using process mining to discover, check, and improve service behavior. IEEE Transactions on Services Computing, 6 (4), 525†535.

Aalst, W. van der (2014. Data scientist: The engineer of the future. In K. Mertins, F. Benaben, R. Poler,

Extracting Event Data from Databases to Unleash Process Mining 125 Aalst, W. van der, Barthelmess, P.,Ellis, C,

Process mining: A two-step approach to balance between underfitting and overfitting. Software and Systems Modeling, 9 (1), 87†111.

Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16 (9), 1128†1142.

Mining process models from workflow logs. In Sixth International Conference on Extending Database Technology (Lecture Notes in Computer science, Vol. 1377, pp. 469†483.

Genetic process mining: An experimental evaluation. Data mining and Knowledge Discovery, 14 (2), 245†304. Barros, A.,Decker, G.,Dumas, M,

Using minimum description length for process mining. In ACM Symposium on Applied Computing (SAC 2009)( pp. 1451†1455.

Log-based transactional workflow mining. Distributed and Parallel Databases, 25 (3), 193†240. Goedertier, S.,Martens, D.,Vanthienen, J,

IEEE Task force on Process Mining. 2011). ) Process mining manifesto. In BPM Workshops (Lecture Notes in Business Information Processing, Vol. 99.

IEEE Task force on Process Mining. 2013a). ) Process mining case studies. Retrieved from http://www. win. tue. nl/ieeetfpm/doku. php?

process mining case studies IEEE Task force on Process Mining. 2013b). ) XES standard definition. Retrieved from www. xes-standard. org Jagadeesh Chandra Bose,

Wanna improve process mining results? It†s high time we consider data quality issues seriously. In B. Hammer, Z. Zhou, L. Wang,

Extracting Event Data from Databases to Unleash Process Mining 127 Reichert, M, . & Weber, B. 2012).

Process mining: Discovery, conformance and enhancement of business processes. Heidelberg: Springer. vom Brocke, J.,Debortoli, S.,Muâ ller, O,

143 Enabling Process Innovation via Deviance Mining and Predictive Monitoring Marlon Dumas and Fabrizio Maria Maggi Abstract A longstanding challenge in the field of business process management

We present two emerging techniques†deviance mining and predictive moni-toring†that leverage information hidden in business process execution logs

or negative deviance (deviance mining) and by continuously estimating the probability that ongoing process executions may lead to undesirable outcomes (predictive monitoring).

In the following section, we outline the architecture of a monitoring system integrating deviance mining and predictive monitoring.

process system that supports deviance mining and predictive monitoring. The figure highlights that both techniques take as input a log of completed business process execution traces and a set of business constraints.

raising flags whenever certain actions heighten the probability of undesirable deviations. 3 Deviance Mining Business process deviance mining is a family of process mining techniques aimed at analyzing business process execution logs

Business Process Suport System Predictive Monitoring Deviance Mining Business Process Execution Log Business Goals Uncompleted Case Process Worker Process Analyst

Recommendations Diagnostics Fig. 1 Business process support with deviance mining and predictive monitoring Enabling Process Innovation via Deviance Mining and Predictive Monitoring 147 A concrete example of negative deviance mining in a large Australian insurance company has been reported by Suriadi,

Wynn, Ouyang, ter Hofstede, and Van dijk (2013. In this case, a team of analysts sought to find the reasons why certain simple claims that should normally be handled within a few days were taking substantially longer to be resolved.

Another case study showing the potential of deviance mining, this time in the healthcare domain, is reported by Lakshmanan, Rozsnyai, and Wang (2013).

The observations made using sequence mining were complemented with additional observations obtained by comparing a process model discovered from cases with positive outcomes with the model obtained for cases with negative outcomes.

The techniques they employ fall under a wider family of techniques known as discriminative sequence mining techniques (Lo, Cheng, & Lucia

the method analyzes the performance of Enabling Process Innovation via Deviance Mining and Predictive Monitoring 149 process workers to determine which process workers perform better for different types of activities.

Enabling Process Innovation via Deviance Mining and Predictive Monitoring 151 Other approaches focus on generating predictions to reduce risks.

In this setting, we position deviance mining and predictive monitoring as two keystones in modern business process support systems.

However, while deviance mining tries to do this off-line (by analyzing process logs), predictive monitoring provides feedback on the fly-fly to prevent violations.

& 152 M. Dumas and F. M. Maggi Yu, 2008) could find useful applications in the context of both deviance mining and predictive monitoring.

Enabling Process Innovation via Deviance Mining and Predictive Monitoring 153 Lo, D.,Cheng, H.,& Lucia.

Mining explicit rules for software process evaluation. In Proceedings of the international conference on software and system process (ICSSP)( pp. 118†125.

Time prediction based on process mining. Information systems, 36 (2), 450†475. Weidlich, M.,Ziekow, H.,Mendling, J.,Guâ nter, O.,Weske, M,

Mining sequence classifiers for early prediction. In Proceedings of the SIAM international conference on data mining (SDM)( pp. 644†655.

Mining reference process models and their configurations. In R. Meersman, Z. Tari, & P. Herrero (Eds.),

Extracting event data from databases to unleash process mining. In J. Brocke & T. Schmiedel (Eds.

, process mining, also reveal several disadvantages in the light of the (typically) conflicting goals of business process management

His research interests include workflow management, process mining, Petri nets, BPM, process modeling, and process analysis. He published more than 175 journal papers, 17 books,

His research interests are business process mining declarative business process modeling and information systems monitoring. He has published close to 50 journal and conference articles in these fields.

His research activities include business process management, process mining, software development as well as implementation of information systems.

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

, 215†226 mining, 13,18, 22, 105†125,147, 249,250, 263,287, 296,302 monitoring, 60,63, 65,83, 146,152 operation, 276, 281†283,285 outcome, 13,272

135 Secondary notation, 186,187, 219†221,223, 225,226 Semantic standardization, 13, 177†189 Sense and respond, 18,22 Sequence mining, 149 Service architecture, 32â€

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

Two Examples 6. 1 Positive Deviance 6. 2 The System That Wasnâ't to Be used 7 Conclusions References Enabling Process Innovation via Deviance Mining

and Predictive Monitoring 1 Introduction 2 Business Process Monitoring Architecture 3 Deviance Mining 4 Predictive Monitoring 5 Discussion and Outlook References Identification

) 73 54.1%â 16,192, 797 57.8%Metals and Engineering 28 20.7%â 6, 033,163 21.5%Mining, Quarrying and Indigenous Services (Health and Education Services;

and metal value chain (2006) International Council on Mining & Metal www. icmm. com/page/1183/maximising-value-guidance-on-implementing-materials-stewardship-in-the-minerals

G#0v 8461 MINING G#1v 9713 Asteroid mining 0#2#asteroid miner Asteroid mining 0#2#asteroid mining Asteroid mining

G#1v 9318 Mining G#2v 9319 Bauxite mining 0#3#bauxite mining Bauxite mining G#2v 9320 Calcium carbonate mining

G#2v 9331 Mining 0#3#mining Mining G#2v 9332 Molybdenum mining 0#3#molybdenum mining Molybdenum mining

G#2v 9333 Nickel mining 0#3#nickel mining Nickel mining G#2v 9334 Non coal mining 0#3#non coal mining Non coal mining

G#1v 9524 Mining techniques G#2v 9525 Borehole mining 0#3#borehole Borehole mining 0#3#borehole mining Borehole mining

Badii A. 2000)' Online Point-of-Click Web Usability Mining with Popeval-MB, Webeval -AB and the C-Assure Methodology'.

Mining & quarrying Manufacturing Utilities Water management Construction Business services Wholesale trade Retail trade Transportatio

Badii A. 2000)' Online Point-of-Click Web Usability Mining with Popeval-MB, Webeval -AB and the C-Assure Methodology'.

contemporary issues †from mining regulations to urban migration †and the dissemination of contemporary film and culture

mining new technologies for social applications In all of these, social innovation is likely to be most successful when there is

International Council on Mining & Metal www. icmm. com/page/1183/maximising-value-guidance-on

"Social network Analysis and Mining: 1-15 Stoica, E. A a. G Pitic, and A i. Tara,"Crawling-A Solution For Efficient E-Government.

or mining and mineral extraction (EUROP, 2009 8. 2 Technological Competitiveness, Industry Links and Market Potentials

From 2005 to 2007 he was a lecturer at China University of Mining and Technology.

and mining of data captured to gain greater customer insights and design more effective sales

mining/manufacturingâ 3 6 2 0â 1 12 Transportâ &â machineryâ operativesâ 0 1 1 1â 10 13

mining resources, carries out a series of actions intended to help involve and encourage companies and institutions

with companies in agribusiness, manufacturing, mining, tourism to provide the investment capital their SME partners need to upgrade, diversify, and scale.

comprising also transport, mining, and chemicals. Dur -ing the Second world war, Germany had established large metal-and-steel and chemical plants for military

-Mining for Real time Detection of Infections 49,000 cases of inpatient HAIS could be avoided every year collectively in all six studied

previous economic reforms and the mining investment boom However, multifactor productivity has stalled for a decade

Mining services ï ï ï Professional & financial services ï ï ï ï Distribution services ï ï

today (BREE, 2014). 2. mining services continues to expand F i rms th at p rov i d e

the demand for mining technology and services will continue to increase our mining advantage is not just about resources in the ground

Groundprobe is a Queensland company with its origins in university research. Its slope stability radar system, manufactured in Brisbane

safety issues, mining, dangerous goods, electrical safety, transport workers, compensation, gas and others (Boral Ltd.

with the mining tax legislation, while not actually paying any tax. The tax cost the ATO over

) Cristal Mining Australia says it could save up to $5 million a year if the current coastal

shipping restrictions were removed (Cristal Mining Australia, 2014 Higher freight costs erode the viability of Australian businesses that use coastal shipping

materials and bauxite-based commodities between mining areas, refineries and smelters BITRE, 2013a Subscribers to broadband internet grew thirteen-fold from 2003-04 to 2011-12 and

as the population ages and the mining investment boom fades. Australia needs to address the

Cristal Mining Australia. 2014). ) Submission to the options paper: Approaches to regulating coastal shipping in Australia.

Cristal Mining Australia Decker, R.,Haltiwanger, J.,Jarmin, R, . & Miranda, J. 2014). The Role of Entrepreneurship in US

•Page 11 †Mining Simulation-VR Space Pty Ltd •Page 38 †Grazing farm animals-istock

ï Montanuniversitã¤t Leoben (Mining University of Graz ï FH Joanneum Gmbh (Polytechnic ï Campus 02 Fachhochschule †Studiengã¤nge der Wirtschaft (Courses for Economy in Campus

fisheries and mining. New zealand excludes electricity, gas and water supply, and only includes enterprises with NZD 30 000 or more in turnover.

mining tools, coupled with real-time data collection over the Internet may provide a whole new

Data Acquisition and Mining: Capturing data on customer requirements and using it to create unique services

and its subsequent analysis or mining can provide a powerful service model for a manufacturer

and mining to lock in customers, suppliers and partners. The fifth â€oemini-case†provides an interesting and illustrative example of a company supplying commodity

profile from salt mining to new fields like tourism, museum and health activities Modernisation Foundry industry (mainly SMES) and steel industry (in both cases, the

implementation and exploitation of ICT and nanotechnologies), mining and energy (e g. clean coal technologies, ICT.

traditional areas of regional specialisation i e. mining (clean coal technologies) or chemistry foundry and steel industries (new materials, ICT

ï Agriculture, Forestry, Fishing, Mining and Quarrying ï Electricity, gas supply, water supply, sewerage, waste management and

ï§Agriculture, Forestry, Fishing, Mining and Quarrying; and ï§Electricity, gas supply, water supply, sewerage, waste management and

†mining support services actionsâ€, †mining of metal ores†and †veterinary activitiesâ€. The main

Mining support service activities Remediation activities and other waste management services Retail trade not in stores, stalls or markets

Mining support service activities Mining of metal ores Office administrative, office support and other business support activities

Activities of head offices; management consultancy activities Remediation activities and other waste management services Veterinary activities

Mining and quarrying Extraction of crude petroleum and natural gas Mining of coal and lignite Mining of metal ores

Mining support service activities Other mining and quarrying Public administration, security and defence Defence Public administration, justice, judicial, public order, fire service and safety activities

Services Activities auxiliary to financial services and insurance activities Activities of head offices and management consultancy activities

Advertising and market research Architectural and engineering activities, technical testing and analysis Education Employment activities Financial service activities, except insurance and pension funding

Insurance, reinsurance and pension funding, except compulsory social security Legal and accounting activities Office administrative, office support and other business support activities

Offshore mining, oil and gas Shipbuilding and ship repair Transport and logistics (including highways of the seas

action, has been mining Twitter data from Indonesia (where Twitter usage is high) 9 to understand food price crises.

mining of big data sources) will not drown out traditional deductive science (i e. hypothesis testing), even in a big data paradigm.

Japan excludes agriculture, forestry, fisheries and mining. New zealand excludes electricity, gas and water supply, and only

exports remain heavily on mining and agriculture 80 O P E N I N N O V A t I O N y E A r B o O k 2 0 1 4

design, mining and interactive data analysis scripting or programming languages, expert systems and machine learning, etc •Knowledge based on mathematics like rela

*Mining and quarrying; Manufacturing; Electricity, gas and water **Wholesale and retail trade; Hotels and restaurants;

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