| Coal mining (1) |  |
| Gold mining (3) | |
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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 (Gu 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)).
Wil van der Aalst reports on Extracting Event Data from Databases to Unleash Process Mining. He introduces an approach to create event logs from underlying databases as a fundamental prerequisite for the application of processmining techniques
Marlon Dumas and Fabrizio Maria Maggi give insights on Enabling Process Innovation via Deviance Mining and Predictive Monitoring.
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 flat 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 redo logs that can be used to reconstruct the history of database updates.
To understand why process-mining techniques need flat 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, flat event logs serve as the starting point for process mining.
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.
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:
Therefore, this paper focuses on the input side of process mining. Before we present our database-centric approach,
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 ðc;
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 flat event log and not a change log as described in Definition 10.
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
EMÞÞINTO 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 PI CL with CL the set of classes.
Consider for example the study process of students taking a particular course. Rather than creating one process model for all students,
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 applicability of process mining. Process mining can be used to check conformance, detect bottlenecks,
and suggest process improvements. However, the most timeconsuming 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. xesstandard. 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.,Mu 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 monitoring 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
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.
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.,Gu 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
manager, 269,277 284 map, 13,14, 215 226,296 benefits, 216 design, 13,14, 215 226 mining, 13,18, 22,105 125,147, 249,250, 263,287, 296,302
226 Semantic standardization, 13,177 189 Sense and respond, 18,22 Sequence mining, 149 Service architecture, 32 33 composition, 43 44 models, 79 81,132
) 73 54.1%€ 16,192, 797 57.8%Metals and Engineering 28 20.7%€ 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-onimplementing-materials-stewardship-in-the-minerals-and-metalsvalue-chain The Higg Index:
Badii A. 2000)' Online Point-of-Click Web Usability Mining with Popeval-MB, Webeval-AB and the C-Assure Methodology'.
Deloitte analysis Column1 All NACE activities related to innovation Agriculture, forestry & fishing Mining & quarrying Manufacturing Utilities Water management Construction Business services
Badii A. 2000)' Online Point-of-Click Web Usability Mining with Popeval-MB, Webeval-AB and the C-Assure Methodology'.
For example, the'Fever Friend'Network in China, an online community engaged in discussions of controversial and contemporary issues from mining regulations to urban migration
and metal value chain (2006) International Council on Mining & Metal www. icmm. com/page/1183/maximising-value-guidance-onimplementing-materials-stewardship-in-the-minerals-and-metalsvalue-chain The Higg Index:
2 Food, beverages and tobacco 2 Leisure, entertainment, media and publishing 3 Mining, oil and gas 2 Retailing 1 Civil service 1 Social services 0
"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 8. 2. 1. Technological Competitiveness Market shares
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 campaigns.
Other trades 1 2 1 0 0 4 Operatives 3 7 3 1 11 25 Process operatives in mining/manufacturing 3
Innovation Trade and Tourism in matters of energy and geological and mining resources, carries out a series of actions intended to help involve
comprising also transport, mining, and chemicals. During the Second world war, Germany had established large metal -and-steel and chemical plants for military needs.
scattered across huge distances, in mining communities which survive on seasonal fly-in, fly-out workforce arrangements, small villages or remote farming properties.
and the resulting slow recovery, benefitting both from previous economic reforms and the mining investment boom.
Agriculture International education Tourism Mining services Professional & financial services Distribution services (a) Deloitte (2014), Positioning for prosperity:
2014). 2. Mining services continues to expand F i r m s t h at p ro v i d e equipment,
As the depth of ore deposits increases and the grade of ore decreases, 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.
VR Space has signed recently a three year partnership agreement with Simtars, a division of the Queensland Department of Natural resources and Mining,
These covered an array of areas include general health and safety issues, mining, dangerous goods, electrical safety, transport workers, compensation, gas and others (Boral Ltd.
but over 125 other miners were complying with the mining tax legislation, while not actually paying any tax.
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 services. These include Australian producers and manufacturers of products like sugar, cement, fertiliser, steel and aluminium,
Bulk ports are crucial for handling the importation and distribution of petroleum, cement materials and bauxite-based commodities between mining areas, refineries and smelters (BITRE, 2013a.
if we are to sustain our economic performance as the population ages and the mining investment boom fades.
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 Job creation and Economic Dynamism.
Page 8 Treetop walk-istock Page 11 Mining Simulation-VR Space Pty Ltd Page 38 Grazing farm animals-istock Page 43
of Graz) Technische Universität Graz (Technical University of Graz) Medizinische Universität Graz (Medical University of Graz) Montanuniversität Leoben (Mining University of Graz) FH Joanneum
Japan excludes agriculture, forestry, fisheries and mining. New zealand excludes electricity, gas and water supply, and only includes enterprises with NZD 30 000 or more in turnover.
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 mini-case provides an interesting and illustrative example of a company supplying commodity chemicals yet providing greater value to customers, and partners.
which has changed the 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.
Regional universities are engaged also in many initiatives aimed at addressing the main challenges in traditional areas of regional specialisation i e. mining (clean coal technologies) or chemistry, foundry and steel industries (new materials, ICT.
Agriculture, Forestry, Fishing, Mining and Quarrying Electricity, gas supply, water supply, sewerage, waste management and remediation;
Agriculture, Forestry, Fishing, Mining and Quarrying; and Electricity, gas supply, water supply, sewerage, waste management and remediation, construction.
Extraction of crude petroleum and natural gas (06) Mining support service activities (09) Manufacture of coke and refined petroleum products (19) Manufacture of basic pharmaceutical products and pharmaceutical preparations
The latter only represents part of GDP as it excludes e g. mining, construction and the public sector. GLSRTK RS CH TR NO HR SI RO PT PL NL AT MT HU LU LV CY IT ES FR EE
'Paper presented at International Conference on Advances in Social network Analysis and Mining. Dachs, B. & Pyka, A. 2010.'
carriers and trucks for mining and cleanup activities. Surrounding these large firms are the far more numerous SME suppliers who form a key part of the supply chains of these larger firms.
there being overlaps in most sectors except for‘mining support services actions',‘mining of metal ores'and‘veterinary activities'.
%18%20%Growth rate of employment Mining support service activitiesmining of metal oresoffice administrative, office support and otherbusiness support activitiesactivities of head offices;
wearing apparel and leather and related products Wood and paper (except for furniture) Other manufacturing Other nonmetallic mineral products 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
growth Aquaculture Blue renewable energy Coastal and maritime tourism Fisheries Marine biotechnology Offshore mining, oil and gas Shipbuilding and ship repair Transport
and humanitarian action, has been mining Twitter data from Indonesia (where Twitter usage is high) 9 to understand food price crises.
(i e. the algorithmic mining of big data sources) will not drown out traditional deductive science (i e. hypothesis testing), even in a big data paradigm.
The mining and raw material industry offer an opportunity for international cooperation with Bosnia and herzegovina in particular in the field of mining machines and technologies,
as well as the raw materials processing machinery and technologies. 69 5. Policy instruments 5. 1. Consistency with the key planning documents and programmes National Reform Programme In the framework of the National Reform Programme,
Japan excludes agriculture, forestry, fisheries and mining. New zealand excludes electricity, gas and water supply, and only includes enterprises with NZD 30 000 or more in turnover.
and highvalue-added manufactured goods. However, its exports remain heavily on mining and agriculture. 80 O P E N I N N O V A t
Computer skills as query languages, database design, mining and interactive data analysis, scripting or programming languages, expert systems and machine learning, etc.
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