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/