19 augustus 2019 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology.

Slides:

Advertisements

Similar presentations

Han-na Yang Trace Clustering in Process Mining M. Song, C.W. Gunther, and W.M.P. van der Aalst.

Advertisements

Process Mining in the Context of Web Services Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.

/faculteit technologie management 1 Process Mining: Organizational and Conformance Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros.

MXML A Meta model for process mining data

/faculteit technologie management 1 Process Mining: Control-Flow Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University.

Discovering Social Networks from Enterprise Data Laks V.S. Lakshmanan Based on: Wil M.P. van der Aalst, Hajo A. Reijers, Minseok Song. Discovering Social.

A Survey of Process Mining in ProM By Jantima Polpinij Decision Systems Lab (DSL) Seminar School of Computer Science and Software Engineering Faculty of.

Boudewijn van Dongen /t Multi-phase process mining Building instance graphs.

/faculteit technologie management Genetic Process Mining Ana Karla Medeiros Ton Weijters Wil van der Aalst Eindhoven University of Technology Department.

Process Mining from discovery to checking Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology, Department of Information Systems, P.O. Box.

/faculteit technologie management Genetic Process Mining Ana Karla Alves de Medeiros Eindhoven University of Technology Department.

Process Mining in CSCW Systems All truths are easy to understand once they are discovered; the point is to discover them. Galileo Galilei ( )

Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department.

1 Analysis of workflows a-priori and a-posteriori analysis Wil van der Aalst Eindhoven University of Technology Faculty of Technology Management Department.

1 Modeling workflows : The organizational dimension and alternative notations. Wil van der Aalst Eindhoven University of Technology Faculty of Technology.

WebMiningResearch ASurvey Web Mining Research: A Survey By Raymond Kosala & Hendrik Blockeel, Katholieke Universitat Leuven, July 2000 Presented 4/18/2002.

Business Alignment Using Process Mining as a Tool for Delta Analysis Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information.

/faculteit technologie management Dutch-Belgian Database Day 2007 The Challenges of Process Mining A.J.M.M. Weijters (and many others)

Process Mining: The next step in Business Process Management Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information.

/faculteit technologie management Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance Wil van der Aalst.

Discovering Coordination Patterns using Process Mining Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information and Technology.

/faculteit technologie management 1 Process Mining: General Introduction Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University of.

Boudewijn van Dongen June 22, 2004 /t Process Mining, the basics.

Process Mining: Discovering processes from event logs All truths are easy to understand once they are discovered; the point is to discover them. Galileo.

/faculteit technologie management Genetic Process Mining Wil van der Aalst Ana Karla Medeiros Ton Weijters Eindhoven University of Technology Department.

Process Mining: An iterative algorithm using the Theory of Regions Kristian Bisgaard Lassen Boudewijn van Dongen Wil van.

1 Workflow/Business Process Management Introduction business process management and workflow management Eindhoven University of Technology Faculty of Technology.

/faculteit technologie management 1 Process Mining: Extension Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University.

Example Data Sets Prior Research Join related objects to form independent compound objects, cluster normally (Yin et al., 2005). Use attribute-based distance.

Process Mining for Ubiquitous Mobile Systems An Overview and a Concrete Algorithm Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department.

A university for the world real R © 2009, Chapter 17 Process Mining and Simulation Moe Wynn Anne Rozinat Wil van der Aalst Arthur.

Process mining Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology, Department of Information Systems, P.O. Box 513, 5600 MB Eindhoven, The.

1 Workflow/Business Process Management Introduction business process management and workflow management Eindhoven University of Technology Faculty of Technology.

Process Mining Control flow process discovery Fabrizio Maria Maggi (based on Process Mining book – Springer copyright 2011 and lecture material by Marlon.

Social Network Analysis: What it Is, How it Works, and How You Can Do It Prof. Paul Beckman San Francisco State University.

Jorge Muñoz-Gama Universitat Politècnica de Catalunya (Barcelona, Spain) Algorithms for Process Conformance and Process Refinement.

Process Mining Control flow process discovery

Process Mining: Discovering processes from event logs All truths are easy to understand once they are discovered; the point is to discover them. Galileo.

Workflow/Business Process Management Introduction business process management and workflow management Wil van der Aalst Eindhoven University of Technology.

Special Topics in Educational Data Mining HUDK5199 Spring 2013 March 25, 2012.

1 Analysis of workflows : Verification, validation, and performance analysis. Wil van der Aalst Eindhoven University of Technology Faculty of Technology.

Han-na Yang Rediscovering Workflow Models from Event-Based Data using Little Thumb.

Decision Mining in Prom A. Rozinat and W.M.P. van der Aalst Joosung, Ko.

Alignment-based Precision Checking A. Adriansyah 1, J. Munoz Gamma 2, J. Carmona 2, B.F. van Dongen 1, W.M.P. van der Aalst 1 Tallinn, 3 September 2012.

/faculteit technologie management Workflow Mining: Current Status and Future Directions Ana Karla A. de Medeiros, W.M.P van der Aalst and A.J.M.M. Weijters.

/faculteit technologie management PN-1 Petri nets refresher Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology, Faculty of Technology Management,

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Towards comprehensive support for organizational mining Presenter : Yu-hui Huang Authors : Minseok Song,

Decision Maths 1 Shortest path algorithm Dijkstra’s Algorithm A V Ali :

Visualization in Process Mining

30 januari 2018 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology.

7 mei 2018 Process Mining in CSCW Systems All truths are easy to understand once they are discovered; the point is to discover them. Galileo Galilei.

MTAT Business Process Management (BPM) Lecture 11: Process Monitoring and Mining Fabrizio Maggi (based on lecture material by Marlon Dumas, Wil.

Profiling based unstructured process logs

SOCIAL NETWORKS Amit Sharma INF -38FQ School of Information

Wil van der Aalst Eindhoven University of Technology

Wil van der Aalst Eindhoven University of Technology

Decomposed Process Mining: The ILP Case

Wil van der Aalst Eindhoven University of Technology

Workflow Management Systems: Functions, architecture, and products.

Wil van der Aalst Eindhoven University of Technology

Petri nets refresher Prof.dr.ir. Wil van der Aalst

Wil van der Aalst Eindhoven University of Technology

Workflow Management Systems: Functions, architecture, and products.

Multi-phase process mining

SOCIAL NETWORKS Amit Sharma INF -38FQ School of Information

3 mei 2019 Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance Wil van der Aalst Ana Karla A. de Medeiros.

Business Alignment Using Process Mining as a Tool for Delta Analysis

Kostas Kolomvatsos, Christos Anagnostopoulos

Neo4j for Process Mining

5 juli 2019 Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance Wil van der Aalst Ana Karla A. de Medeiros.

Presentation transcript:

19 augustus 2019 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information and Technology P.O. Box 513, 5600 MB Eindhoven The Netherlands w.m.p.v.d.aalst@tm.tue.nl 1 Joint work with Minseok Song, Ana Karla Alves de Medeiros, Boudewijn van Dongen, Ton Weijters, et al.

Outline Motivation Process mining Social network analysis Metrics Overview Classification Tooling Social network analysis Metrics MiSoN Application Conclusion

Motivation Process-aware information systems (WFMS, BPMS, ERP, SCM, B2B) log events. Many event logs also record the “performer”. Social Network Analysis (SNA) started in the 30-ties (Moreno) and resulted in mature methods and tools for analyzing social networks. Process Mining (PM) is a new technique to extract knowledge from event logs. Research question: Can we combine SNA and PM?

process mining Process mining Process mining can be used for: Process discovery (What is the process?) Delta analysis (Are we doing what was specified?) Performance analysis (How can we improve?) www.processmining.org

Process mining: Overview 2) process model 3) organizational model 4) social network 1) basic performance metrics 5) performance characteristics 6) auditing/security If …then …

Process Mining: Tooling

Social Network Analysis Started in 30-ties (Moreno). Graph where nodes indicate actors (performers/individuals). Edges link actors and may be directed and/or weighted. Metrics for the graph as a whole: density Metrics for actors: Centrality (shortest path/path through) Closeness (1/sum of distances) Betweenness (paths through) Sociometric status (in/out) John Mary Bob Clare June

Metrics Each event refers to a case, a task and a performer (event type, data, and time are optional). Four types of metrics: Metrics based on (possible) causality Metrics based on joint cases Metrics based on joint activities Metrics based on special event types

Example: Metrics based on (possible) causality Hand-over of work metrics In-between metrics (subcontracting)

Hand-over of work metrics: Parameters Real causality or not? Consider hand-overs that are indirect? (If so, add causality fall factor.) Consider multiple transfers within one case? Note that there are at least 8 variants.

MiSoN (Mining Social Networks) tool Uses standard XML format (www.processmining.org) Adapters for Staffware, FLOWer, MQSeries, ARIS, etc. Interfaces with SNA tools like AGNA, NetMiner, etc.

Real analysis in SNA tools types of metrics graph view Screenshot matrix view Real analysis in SNA tools operations supported

Case study Only preliminary results Dutch national works department (1000 workers) Responsible for construction and maintenance of infrastructure in province. Process: Processing of invoices from the various subcontractors and suppliers Log: 5000 cases and 33.000 events. Focus on 43 key players

SN based on hand-over of work metric density of network is 0.225

Ranking of performers Ranking Name Betweenness IN-Closeness OUT-Closeness Power 1 rogsp 0.152 0.792 jansgtam 0.678 bechccm 4.102 2 0.141 0.667 2.424 3 0.085 prijlgm 0.75 0.656 hulpao 1.964 4 eerdj 0.079 0.689 0.635 groorjm 1.957 5 0.065 frida schicmm 0.625 hopmc 1.774 … 39 ernser, broeiba, fijnc, hulpao, blomm, berkmhf, piermaj, passhgjh, beheerder1 blomm berkmhf 0.381 passhgjh 0.001 40 0.331 timmmcm 0.385 0.005 41 piermaj 0.375 0.404 poelml 0.007 42 fijnc 0.382 0.417 43 leonie 0.426 0.009 Ranking of performers

SN based on subcontracting

SN based on working together (and ego network)

SN based on joint activities

SN based on hand-over of work between groups

Relating tasks and performers (using correspondence analysis)

Conclusion Combining process mining and SNA provides interesting results. MiSoN enables the application of SNA tools based on “objective data”. There are many challenges: Applying PM/SNA in organizations Improving the algorithms (hidden/duplicate tasks, …) Gathering the data Visualizing the results Etc. Join us at www.processmining.org

More information http://www.workflowcourse.com http://www.workflowpatterns.com http://www.processmining.org W.M.P. van der Aalst and K.M. van Hee. Workflow Management: Models, Methods, and Systems. MIT press, Cambridge, MA, 2002/2004.