Beyond Tasks and Gateways: Discovering BPMN Models with subprocesses, boundary events and activity markers Raffaele Conforti, Marcello La Rosa Queensland.

Slides:



Advertisements
Similar presentations
Queensland University of Technology – University of Tartu From Conceptual to Executable BPMN Process Models A Step-by-Step.
Advertisements

BPMN to Mapping of BPMN diagrams to YAWL for execution out of Oryx Armin Zamani Farahani May 26th, 2009.
Han-na Yang Trace Clustering in Process Mining M. Song, C.W. Gunther, and W.M.P. van der Aalst.
A Case-based Approach to Business Process Monitoring S. Montani 1, G. Leonardi 1 1 Dipartimento di Informatica, University of Piemonte Orientale, Alessandria,
/faculteit technologie management 1 Process Mining: Organizational and Conformance Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros.
Marlon Dumas marlon.dumas ät ut . ee
Business Process Modelling -9.2/ Marcello La Rosa Queensland University of Technology Brisbane, 19 September 2013.
/faculteit technologie management 1 Process Mining: Control-Flow Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University.
1 Introduction to modeling Process modelling. 2 Where are we? #TitleDate 1Introduction ORM modeling Relational modeling
Process discovery: Inductive Miner
Aligning Event Logs and Process Models for Multi- perspective Conformance Checking: An Approach Based on ILP Massimiliano de Leoni Wil M. P. van der Aalst.
Beyond Process Mining: Discovering Business Rules From Event Logs Marlon Dumas University of Tartu, Estonia With contributions from Luciano García-Bañuelos,
Block-Structured Process Discovery: Filtering Infrequent Behaviour Sander Leemans Dirk Fahland Wil van der Aalst Eindhoven University of Technology.
A Survey of Process Mining in ProM By Jantima Polpinij Decision Systems Lab (DSL) Seminar School of Computer Science and Software Engineering Faculty of.
/faculteit technologie management Dutch-Belgian Database Day 2007 The Challenges of Process Mining A.J.M.M. Weijters (and many others)
Classification II.
Process Mining: Discovering processes from event logs All truths are easy to understand once they are discovered; the point is to discover them. Galileo.
BPMN An Introduction ISIS. © ILOG, All Rights Reserved 2 Definition of BPMN Business Process Modeling Notation provides:  The capability of defining.
© INB/INN /2012 – 25 July 2013 Your Unit Coordinator A/Professor Marcello La Rosa Academic Director (corporate programs and partnerships) for IS.
Business Process Management with Activiti João Silva (CERN, GS-AIS) 21st of October, 2014 BUSINESS PROCESS MANAGEMENT WITH ACTIVITI.
A university for the world real R © 2009, Chapter 13 The Business Process Management Notation Gero Decker Remco Dijkman Marlon Dumas.
Unraveling Unstructured Process Models Marlon Dumas University of Tartu, Estonia Joint work with Artem Polyvyanyy and Luciano García-Bañuelos Invited Talk,
BPMN to Mapping of BPMN diagrams to YAWL for execution out of Oryx Armin Zamani Farahani July 10th, 2009.
Department of Computer Science 1 CSS 496 Business Process Re-engineering for BS(CS)
Department of Computer Science 1 CSS 496 Business Process Re-engineering for BS(CS)
Marlon Dumas marlon.dumas ät ut . ee
Marlon Dumas University of Tartu
Insuring Sensitive Processes through Process Mining Jorge Munoz-Gama Isao Echizen Jorge Munoz-Gama and Isao Echizen.
Data Mining CMPT 455/826 - Week 10, Day 2 Jan-Apr 2009 – w10d21.
Scientific Workflows Within the Process Mining Domain Martina Caccavale 17 April 2014.
Dr Marcello La Rosa BPM Research Group, Queensland University of Technology.
Oracle Data Integrator Changed Data Capture.
Process Mining Control flow process discovery Fabrizio Maria Maggi (based on Process Mining book – Springer copyright 2011 and lecture material by Marlon.
Model Transformations for Business Process Analysis and Execution Marlon Dumas University of Tartu.
Process Mining Control flow process discovery
SiS Technical Training Development Track Day 9. Agenda  Understand Workflow Technology  Practice of Workflow (Instructor Led)
Chapter 12: Web Usage Mining - An introduction Chapter written by Bamshad Mobasher Many slides are from a tutorial given by B. Berendt, B. Mobasher, M.
Petri nets refresher Prof.dr.ir. Wil van der Aalst
Process-oriented System Analysis Process Mining. BPM Lifecycle.
Chapter 6 Classification and Prediction Dr. Bernard Chen Ph.D. University of Central Arkansas.
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.
Marlon Dumas University of Tartu
COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Monitoring in TOSCA – Future Use Cases Presenter: Ifat Afek, Alcatel-Lucent September 2015.
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 Classification: predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values.
Activiti Dima Ionut Daniel. Contents What is Activiti? Activiti Basics Activiti Explorer Activiti Modeler Activiti Designer BPMN 2.0 Activiti Process.
Instance Discovery and Schema Matching With Applications to Biological Deep Web Data Integration Tantan Liu, Fan Wang, Gagan Agrawal {liut, wangfa,
Prof. Marcello La Rosa BPM Discipline Queensland University of Technology.
2 The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any.
MTAT Business Process Management Lecture 3 – Process Modeling II Marlon Dumas marlon.dumas ät ut. ee 1.
Prof. Marcello La Rosa BPM Discipline Queensland University of Technology.
Learning Procedural Knowledge through Observation -Michael van Lent, John E. Laird – 인터넷 기술 전공 022ITI02 성유진.
Discovering Models for State-based Processes M.L. van Eck, N. Sidorova, W.M.P. van der Aalst.
Business Process Modelling
The Automated Discovery of Hybrid Processes Fabrizio M. Maggi University of Tartu Tijs Slaats* IT University of Copenhagen Exformatics Hajo A. Reijers.
Prof. Marcello La Rosa BPM Discipline Queensland University of Technology.
Profiling and process mining What has been done???
CRICOS No J a university for the world real R 1 Prof. Marcello La Rosa BPM Discipline Queensland University of Technology.
Business process management (BPM)
Discovering high-level models and working with BPMN in ProM
Profiling based unstructured process logs
Exploring processes and deviations
Business process management (BPM)
David Redlich, Thomas Molka, Wasif Gilani, Awais Rashid, Gordon Blair
Chapter 6 Classification and Prediction
Patterns extraction from process executions
Chapter 10: Process Implementation with Executable Models
Decomposed Process Mining: The ILP Case
Marlon Dumas marlon.dumas ät ut . ee
Presentation transcript:

Beyond Tasks and Gateways: Discovering BPMN Models with subprocesses, boundary events and activity markers Raffaele Conforti, Marcello La Rosa Queensland University of Technology Marlon Dumas, Luciano García-Bañuelos University of Tartu 1 BPM’2014 Conference, Eindhoven 11 September 2014

2 CIDTaskTime Stamp… 13219Enter Loan Application T 11:20: Retrieve Applicant Data T 11:22: Enter Loan Application T 11:22: Compute Installments T 11:22: Notify Eligibility T 11:23: Approve Simple Application T 11:24: Compute Installements T 11:24:35- ………… Automated Process Discovery

What’s the catch?

There you are!

Automated Process Discovery: Handling Complexity Filter Filter out “irrelevant” events (tasks) Filter out “irrelevant” traces Abstract Zoom into most frequent tasks or paths Extract subprocesses Divide Divide log by variants based on similarity (trace clustering) Discover multiple process models rather than one 5

Bose, Veerbeck & van det Aalst: Discovering Hierarchical Process Models using ProM Related Work: ProM two-phase miner

ProM Two-Phase Miner 7 Instead of… Produces this…

Before 8

After 9

What’s the catch?

Data! 11

Extracting the Process Hierarchy Extract event tables Find primary keys Find foreign keys Cluster event types Split log per cluster 12 flat log log hierarchy TimePOIDAtt2Att :121…… :532…… ………… ReceivePO TimeMOIDPOIDAtt :1311… :3122… …2…… CreateMO ShipPO TimePOIDAtt :11 1… …2… ………

… the rest Discover one model per (sub- )process Identify interrupting boundary events Identify interrupting timer events Identify event subprocesses Identify loop/multi- instance markers 13 -Heuristics miner -ILP -Inductive Miner -Fodina Heuristics

Evaluation Setup Four flat process discovery algos Heuristics, ILP, Inductive (H), Fodina Each algo with and without BPMN-Miner Quality measures Accuracy: Fitness, precision, F-score Understandability: Size, Control-Flow Complexity, … LogsTracesEventsEvent typesEvents/type IWT (FRIS) Insurance Order-to-cash

Evaluation - Results 15

Side-Effect: Correct Models 16

What’s Next Standalone tool implementation Currently in ProM nightly build Further evaluation Logs with larger number of event types Noise resilience Missing events can trick foreign key discovery Further enrichment Event-based gateways, more BPMN events… Adding data conditions, completion conditions, … 17