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

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

A university for the world real R © 2009, Chapter 3 Advanced Synchronization Moe Wynn Wil van der Aalst Arthur ter Hofstede.
Sequential Patterns & Process Mining Current State of Research Edgar de Graaf LIACS.
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.
Towards Workflow Pattern Support of Event-Driven Process Chains (EPC) Jan Mendling, Gustaf Neumann Dept. of IS and New Media, WU Wien, Austria Markus Nüttgens.
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
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.
/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.
1 Process-Aware Information Systems Dumas, van der Aalst, ter Hofstede UC San Diego CSE 294 December 3, 2009 Barry Demchak.
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.
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.
Petri nets refresher Prof.dr.ir. Wil van der Aalst
1 Patterns and Products Wil van der Aalst Eindhoven University of Technology Faculty of Technology Management Department of Information and Technology.
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.
A university for the world real R © 2009, Chapter 12 The Declare Service Maja Pesic Helen Schonenberg Wil M.P. van der Aalst.
Maikel Leemans Wil M.P. van der Aalst. Process Mining in Software Systems 2 System under Study (SUS) Functional perspective Focus: User requests Functional.
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.
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.
Profiling based unstructured process logs
David Redlich, Thomas Molka, Wasif Gilani, Awais Rashid, Gordon Blair
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
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
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:

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

Outline BPM research in Eindhoven Process Mining ProM –Architecture –Process mining plug-ins Alpha-algorithm Multi-phase mining Genetic mining –Analysis plug-ins –Conformance testing plug-in –LTL checker plug-in –Social network plug-in –Convertors ( , Staffware, InConcert, SAP, etc.) Conclusion

BPM research in Eindhoven

Computer Science/information Systems in Eindhoven TU/e TM (Technology Management) MCS (Math. and Comp. Science) IS (Information Systems) CS (Computer Science) BPM (Business Process Management) Math. ICTA SE IS (Information Systems) BETA

Research within the BPM group Workflow management systems Process modeling (design and redesign) Process analysis (validation, verification, and performance analysis) Process mining Often based on Petri nets

Analysis and improvement of resource-constrained workflow processes Mariska Netjes

Product-driven process design Irene Vanderfeesten

Improving work distribution mechanisms in WFM systems Maja Pesic

Patterns in process-aware information systems Nataliya Mulyar

Genetic process mining Ana Karla Alves de Medeiros

Process mining in case handling systems Christian Günther

Process mining: a formal approach Boudwijn van Dongen

Verification of workflow processes Eric Verbeek

YAWL: Yet Another Workflow Language

Arthur ter Hofstede (patterns, YAWL def.) Marlon Dumas (BABEL, SOC) Lachlan Aldred (YAWL engine, comm. pats) Lindsay Bradford (YAWL designer) Tore Fjellheim (YAWL logging, timers) Guy Redding (YAWL forms) Nick Russell (work distribution/transactions) Moe Wynn (verification, optimization) Michael Adams (flexibility)

Process Mining

Motivation: Reversing the process 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?) process mining

Overview 1) basic performance metrics 2) process model3) organizational model4) social network 5) performance characteristics If …then … 6) auditing/security

Let us focus on mining process models … 1) basic performance metrics 2) process model3) organizational model4) social network 5) performance characteristics If …then … 6) auditing/security... and a very simple approach: The alpha algorithm

Process log 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 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

>, ,||,# relations 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. 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 ABACBDCDEFABACBDCDEF B||C C||B

Basic idea (1) xyx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.  (W) is defined as follows. 1.T W = { t  T     W t   }, 2.T I = { t  T     W t = first(  ) }, 3.T O = { t  T     W t = last(  ) }, 4.X W = { (A,B)  A  T W  B  T W   a  A  b  B a  W b   a1,a2  A a 1 # W a 2   b1,b2  B b 1 # W b 2 }, 5.Y W = { (A,B)  X   (A,B)  X A  A  B  B  (A,B) = (A,B) }, 6.P W = { p (A,B)  (A,B)  Y W }  {i W,o W }, 7.F W = { (a,p (A,B) )  (A,B)  Y W  a  A }  { (p (A,B),b)  (A,B)  Y W  b  B }  { (i W,t)  t  T I }  { (t,o W )  t  T O }, and  (W) = (P W,T W,F W ). The alpha algorithm has been proven to be correct for a large class of free-choice nets.

Example 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  (W) W

Challenges Refining existing algorithm for (control-flow/process perspective) –Hidden tasks –Duplicate tasks –Non-free-choice constructs –Loops –Detecting concurrency (implicit or explicit) –Mining and exploiting time –Dealing with noise –Dealing with incompleteness Mining other perspectives (data, resources, roles, …) Gathering data from heterogeneous sources Visualization of results Delta analysis

DEMO Alpha algorithm 48 cases 16 performers

ProM framework

ProM

Converter plug-in: Analyzer

XML format

ProM architecture

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: Genetic Miner

Overview of approach

Analysis 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. Involves multiple perspectives (process, data, resources, etc.) Get ProM-ed! You can contribute by applying ProM and developing plug-ins

More information