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.

Slides:



Advertisements
Similar presentations
DecSerFlow Towards a Truly Declarative Service Flow Language Wil van der Aalst & Maja Pesic Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven,
Advertisements

Jorge Muñoz-Gama Josep Carmona
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.
Process discovery: Inductive Miner
Data Conformance Checking using Optimal Alignments Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers.
Block-Structured Process Discovery: Filtering Infrequent Behaviour Sander Leemans Dirk Fahland Wil van der Aalst Eindhoven University of Technology.
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.
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.
Boudewijn van Dongen April 27, 2005 The ProM-framework A framework for integrating process mining tools.
/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.
/faculteit technologie management 1 Process Mining: Extension Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University.
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.
A university for the world real R © 2009, Chapter 23 Epilogue Wil van der Aalst Michael Adams Arthur ter Hofstede Nick Russell.
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.
Insuring Sensitive Processes through Process Mining Jorge Munoz-Gama Isao Echizen Jorge Munoz-Gama and Isao Echizen.
Scientific Workflows Within the Process Mining Domain Martina Caccavale 17 April 2014.
Process Mining Control flow process discovery Fabrizio Maria Maggi (based on Process Mining book – Springer copyright 2011 and lecture material by Marlon.
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.
Petri nets refresher Prof.dr.ir. Wil van der Aalst
Process-oriented System Analysis Process Mining. BPM Lifecycle.
Content Outline Introduction by Example The Project
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.
Intelligent Database Systems Lab N.Y.U.S.T. I. M. Towards comprehensive support for organizational mining Presenter : Yu-hui Huang Authors : Minseok Song,
1 CS techniques for IT auditing Lecture 6. Dept of Mathematics and Computer Science 2 Transition system (1) Basic process model of CS is a transition.
Process Mining – Concepts and Algorithms Review of literature on process mining techniques for event log data.
Multi-phase Process Mining: Building Instance Graphs
30 januari 2018 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology.
YAWL in the Cloud: Supporting Process Sharing and Variability
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
David Redlich, Thomas Molka, Wasif Gilani, Awais Rashid, Gordon Blair
Length of a Segment Let A and B be points on a number line, with coordinates a and b. Then the measure of segment AB is.
Wil van der Aalst Eindhoven University of Technology
Decomposed Process Mining: The ILP Case
بررسی شباهت مدل فرآیندها گزارش سمینار کارشناسی ارشد
Wil van der Aalst Eindhoven University of Technology
Workflow Management Systems: Functions, architecture, and products.
Wil van der Aalst Eindhoven University of Technology
Wil van der Aalst Eindhoven University of Technology
Workflow Management Systems: Functions, architecture, and products.
Multi-phase process mining
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
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.
Faulty EPCs in the SAP Reference Model
19 augustus 2019 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology.
Presentation transcript:

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 (1564 - 1642) 1 Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information Systems P.O. Box 513, 5600 MB Eindhoven The Netherlands w.m.p.v.d.aalst@tm.tue.nl

Outline CSCW spectrum Process Mining ProM Conclusion spectrum 7 mei 2018 Outline CSCW spectrum spectrum motivation Process Mining overview a concrete algorithm: the alpha algorithm ProM Architecture Convertors (e-mail, Staffware, InConcert, SAP, etc.) Process mining plug-ins Analysis plug-ins Conformance testing plug-in LTL checker plug-in Social network plug-in Conclusion

we want to apply process mining to each of these domains ... CSCW spectrum we want to apply process mining to each of these domains ...

process mining is relevant across the whole spectrum

Motivation BAM (Business Activity Monitoring), BOM (Business Operations Management), BPI (Business Process Intelligence) illustrate the interest in process improvement based on monitoring data. Systems need to be adaptive and self-managing thus increasing the need for monitoring. Legislation such as the Sarbanes-Oxley Act, is forcing organizations to monitor activities and processes. Technology push: The data is there and more will come! (cf. ERP systems, RFID, webservices, etc.).

Process Mining

Motivation: Reversing the process 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?)

Overview www.processmining.org If …then … 2) process model 3) organizational model 4) social network 1) basic performance metrics 5) performance characteristics 6) auditing/security If …then … www.processmining.org

Let us focus on mining process models … 3) organizational model 4) social network 1) basic performance metrics 5) performance characteristics 6) auditing/security If …then … ... and a very simple approach: The alpha algorithm

Alpha algorithm α

Process log Minimal information in log: case id’s and task id’s. case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D Minimal information in log: case id’s and task id’s. Additional information: event type, time, resources, and data. In this log there are three possible sequences: ABCD ACBD EF

>,,||,# relations case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D A>B A>C B>C B>D C>B C>D E>F Direct succession: x>y iff for some case x is directly followed by y. Causality: xy iff x>y and not y>x. Parallel: x||y iff x>y and y>x Choice: x#y iff not x>y and not y>x. B||C C||B AB AC BD CD EF

Basic idea (1) xy

Basic idea (2) xy, xz, and y||z

Basic idea (3) xy, xz, and y#z

Basic idea (4) xz, yz, and x||y

Basic idea (5) xz, yz, and x#y

It is not that simple: Basic alpha algorithm Let W be a workflow log over T. a(W) is defined as follows. TW = { t Î T  |  $s Î W t Î s}, TI = { t Î T  |  $s Î W t = first(s) }, TO = { t Î T  |  $s Î W t = last(s) }, XW = { (A,B) |  A Í TW  Ù B Í TW  Ù  "a Î A"b Î B a ®W b   Ù  "a1,a2 Î A a1#W a2  Ù  "b1,b2 Î B b1#W b2 }, YW = { (A,B) Î X  |  "(A¢,B¢) Î XA Í A¢ ÙB Í B¢Þ (A,B) = (A¢,B¢) }, PW = { p(A,B)  |  (A,B) Î YW } È{iW,oW}, FW = { (a,p(A,B))  |  (A,B) Î YW  Ù a Î A }  È { (p(A,B),b)  |  (A,B) Î YW  Ù b Î B }  È{ (iW,t)  |  t Î TI}  È{ (t,oW)  | t Î TO}, and a(W) = (PW,TW,FW). The alpha algorithm has been proven to be correct for a large class of free-choice nets.

Example W a(W) case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D a(W)

DEMO Alpha algorithm 48 cases 16 performers

ProM framework

ProM

Converter plug-in: EMailAnalyzer

XML format

ProM architecture

Mining plug-in: Alpha algorithm

Mining plug-in: Genetic Miner

Approach

Mining plug-in: Multi-phase mining

Step 1: Get instances

Step 2: Project

Step 3: Aggregate

Step 4: Map onto EPC

Step 5: Map onto Petri net (or other language)

Mining plug-in: Social network miner

Cliques

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

SN based on working together (and ego network)

Analysis plug-in: LTL checker

Analysis plug-in: Conformance checker Do they agree?

Fitness is not enough

Screenshot (Also runs on Mac.)

Other analysis plug-ins

More demos?

Conclusion Process mining provides many interesting challenges for scientists, customers, users, managers, consultants, and tool developers. Interesting across the whole CSCW spectrum. Get ProM-ed! You can contribute by applying ProM and developing plug-ins.

Thanks to Ton Weijters, Boudewijn van Dongen, Ana Karla Alves de Medeiros, Minseok Song, Laura Maruster, Eric Verbeek, Monique Jansen-Vullers, Hajo Reijers, Michael Rosemann, Anne Rozinat, Christian Guenther Peter van den Brand, Huub de Beer, Andrey Nikolov, et al. for their on-going work on process mining.

BPM 2005, Sept., Nancy France http://bpm2005.loria.fr/ More information http://www.workflowcourse.com http://www.workflowpatterns.com http://www.processmining.org BPM 2005, Sept., Nancy France http://bpm2005.loria.fr/