Process Mining An index to the state of the art and an outline of open research challenges at DIIAG
|
|
- Geraldine Lloyd
- 8 years ago
- Views:
Transcription
1 An index to the state of the art and an outline of open research challenges at DIIAG Claudio Di Ciccio, Massimo Mecella Seminars in Software and Services for the Information Society Rome, 2012, May the 7 th
2 Definition [Aalst2011.book], also referred to as Workflow Mining, is the set of techniques that allow the extraction of process descriptions, stemming from a set of recorded real executions (logs). ProM [AalstEtAl2009] is one of the most used plugin based software environment for implementing workflow mining (and more) techniques. The new version 6.0 is available for download at P. 2
3 Definition involves: Process discovery Control flow mining, organizational mining, decision mining; workflow Conformance checking Operational support We will focus on the control flow mining Many control flow mining algorithms proposed α [AalstEtAl2004] and α ++ [WenEtAl2007] Fuzzy [GüntherAalst2007] Heuristic [WeijtersEtAl2001] Genetic [MedeirosEtAl2007] Two-step [AalstEtAl2010] P. 3
4 Further reading The rest of the lesson is based on the following material: Van der Aalst, W. M. P.: Process Discovery: An Introduction Available at process_mining_chapter_05_process_discovery.pdf From the teaching material for [Aalst2011.book] De Medeiros, A. K. A.: : Control-Flow Mining Algorithms Available at lecture3_controlflowminingalgorithms.ppt P. 4
5 A different context (1) Artful processes and knowledge workers Artful processes [HillEtAl06] informal processes typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.) knowledge workers [ACTIVE09] Knowledge workers create artful processes on the fly Though artful processes are frequently repeated, they are not exactly reproducible, even by their originators, nor can they be easily shared. P. 5
6 A different context (2) conversations In collaborative contexts, knowledge workers share their information and outcomes with other knowledge workers E.g., a software development mgr. Typically, by means of several conversations conversations are actual traces of running processes that knowledge workers adhere to P. 6
7 A different context (3) Processes from conversations From the collection of messages, you can extract the processes that lay behind Related conversations are traces of their runs Valuable advantages for users Automated discovery of formal representations with no effort for knowledge workers Tidy organization for naïve best practices kept only in mind Opportunity to share and compare the knowledge on methodologies Automated discovery of bottlenecks, delays, structural defects from the analysis of previous runs conversations are a kind of semi-structured text this approach is not tailored to the electronic mail it can be extended to the analysis of other semi-structured texts P. 7
8 A different context (4) Some areas of applicability Personal information management (PIM) how to organize one s own activities, contacts, etc. through the usage of software Information warfare in supporting anti-crime intelligence agencies Enterprise engineering for knowledge-heavy industries, where preserving documents making up product data is not enough ehealth for the automatic discovery of medical treatment procedures on top of patient health records P. 8
9 MailOfMine What is MailOfMine? MailOfMine is the approach and the implementation of a collection of techniques, the aim of which is to is to automatically build, on top of a collection of messages, a set of workflow models that represent the artful processes laying behind the knowledge workers activities. [DiCiccioEtAl11] [DiCiccioMecella12] [DiCiccioMecella/TR12] P. 9
10 On the visualization of processes The imperative model Represents the whole process at once The most used notation is based on a subclass of Petri Nets (namely, the Workflow Nets) P. 10
11 On the visualization of processes The declarative model If A is performed, B must be perfomed, no matter before or afterwards (responded existence) Rather than using a procedural language for expressing the allowed sequence of activities, it is based on the description of workflows through the usage of constraints the idea is that every task can be performed, except the ones which do not respect such constraints this technique fits with processes that are highly flexible and subject to changes, such as artful processes The notation here is based on [AalstEtAl06, MaggiEtAl11] (DecSerFlow, Declare) Whenever B is performed, C must be performed afterwards and B can not be repeated until C is done (alternate response) P. 11
12 On the visualization of processes Imperative vs declarative Declarative Imperative Declarative models work better in presence of a partial specification of the process scheme P. 12
13 Declare constraint templates Existence templates Existence(n, A) Activity A occurs at least n times in the process instance BCAAC BCAAAC BCAC (for n = 2) Absence(A) Activity A does not occur in the process instance BCC BCAC Absence(n+1, A) Activity A occurs at most n+1 times in the process instance BCAAC BCAC BCC (for n = 2) Exactly(n, A) Activity A occurs exactly n times in the process instance BCAAC BCAAAC BCAC (for n = 2) Init(A) Activity A is the first to occur in each process instance BCAAC ACAAAC BCC P. 13
14 Declare constraint templates Relation templates RespondedExistence(A, B) If A occurs in the process instance, then B occurs as well CAC CAACB BCAC BCC Response(A, B) If A occurs in the process instance, then B occurs after A BCAAC CAACB CAC BCC AlternateResponse(A, B) Each time A occurs in the process instance, then B occurs afterwards, before A recurs BCAAC CAACB CACB CABCA BCC CACBBAB ChainResponse(A, B) Each time A occurs in the process instance, then B occurs immediately afterwards BCAAC BCAABC BCABABC P. 14
15 Declare constraint templates Relation templates RespondedExistence(B, A) If B occurs in the process instance, then A occurs as well CAC CAACB BCAC BCC Precedence(A, B) B occurs in the process instance only if preceded by A BCAAC CAACB CAC BCC AlternatePrecedence(A, B) Each time B occurs in the process instance, it is preceded by A and no other B can recur in between BCAAC CAACB CACB CABCA BCC CACBAB ChainPrecedence(A, B) Each time B occurs in the process instance, then B occurs immediately beforehand BCAAC BCAABC CABABCA P. 15
16 Declare constraint templates Relation templates CoExistence(A, B) If B occurs in the process instance, then A occurs, and viceversa CAC CAACB BCAC BCC Succession(A, B) A occurs if and only if it is followed by B in the process instance BCAAC CAACB CAC BCC AlternateSuccession(A, B) A and B occur in the process instance if and only if the latter follows the former, and they alternate each other in the trace BCAAC CAACB CACB CABCA BCC CACBAB ChainSuccession(A, B) A and B occur in the process instance if and only if the latter immediately follows the former BCAAC BCAABC CABABC P. 16
17 Declare constraint templates Negative relation templates NotCoExistence(A, B) A and B never occur together in the process instance CAC CAACB BCAC BCC NotSuccession(A, B) A can never occur before B in the process instance BCAAC CAACB CAC BCC NotChainSuccession(A, B) A and B occur in the process instance if and only if the latter does not immediately follows the former BCAAC BCAABC CBACBA P. 17
18 Relation constraint templates subsumption Constraint templates are not independent of each other P. 18
19 Relation constraint templates subsumption Constraint templates are not independent of each other E.g., A trace like ABABCABCC satisfies (w.r.t. A and B): RespondedExistence(A, B), RespondedExistence(B, A), CoExistence(A, B), CoExistence(B, A), Response(A, B), AlternateResponse(A, B), ChainResponse(A, B), Precedence(A, B), AlternatePrecedence(A, B), ChainPrecedence(A, B), Succession(A, B), AlternateSuccession(A, B), ChainSuccession(A, B) The mining algorithm would show the most strict constraint only (ChainSuccession(A, B)) MINERful, the mining algorithm of MailOfMine, faces this unresolved issue P. 19
20 MINERful The declarative workflow mining algorithm of MailOfMine Key idea: building a knowledge base with local and global statistics on the mutual order of appearance of events for further fast querying Performances: the algorithm is proven to be fast (over 12m events processed in less than 170 secs.) Asymptotically: linear in the number of the traces quadratic in the number of events per trace i.e., polynomial in the input size linear in the number of constraint templates See [DiCiccioMecella/TR12] for further reading P. 20
21 LTL semantics ConDec, DecSerFlow and Declare adopt Lineartime Temporal Logic (LTL) for expressing the semantics of the constraint templates. See [AalstEtAl06, MaggiEtAl11] for further reading Van der Aalst, W. M. P. : Auditing 2.0 Using to Support Tomorrow's Auditor Available at process-mining-and-auditing-siks-course wvda.pdf See slides P. 21
22 On the representation of artful process schemata Regular grammars expressing declarative workflows In MailOfMine, each constraint in the set which can be used to define an artful mined process is expressible through regular grammars, where: activities are terminal characters, building blocks of constraints on tasks; constraints are regular expressions, equivalent to regular grammars; the process scheme is the intersection of constraints defined on top of activities. The process scheme defines a Process Describing Grammar (PDG) P. 22
23 On the usage of regular grammars The rationale: why not LTL for declarative workflows? Temporal logic is a formalism for describing sequences of transitions between states in a reactive system Linear Temporal Logic (LTL, [Pnueli77]) describes events along a single computation path LTL formulæ are verified over semi-infinite runs defined over Kripke structures They are good for automatically checking the correct work of circuits or server programs Not for human processes which have both a starting point and an end In the long run, we are all dead ' (John Maynard Keynes) Regular grammars are verified by Finite State Automata working with less complex algorithms, in terms of computational effort A PDG describes the language spoken by collaborative organisms in terms of activities P. 23
24 On the usage of regular grammars Constraint templates as regular expressions P. 24
25 On the visualization of processes An example of DecSerFlow [VanDerAalstEtAl06] notation You could even start from here No, it is not the initial action You might want to run a legal trace like this: a3, a3, a3, a2, a2, a3, a4, a5, a6, a7, a6, a5 What we want to state here is that such a notation is probably not quite intuitive P. 25
26 On the visualization of processes Our proposal We do not consider a static graph-based global representation alone the best suitable solution. A graphical representation, easy to understand at a first glimpse, must be used. Idea: when presenting the process schema (static view): 1) a local view on tasks/activities, showing related constraints only; 2) a global view on the process, either: a) basic (less information, less symbols), or b) extended (more information, more symbols, extending (a)); (2) can work as a kind of navigation map for (1) when presenting the running instance (dynamic view): a dynamic interactive trace representation diagram, based on the local static view notation. See [DiCiccioEtAl2011] for further reading P. 26
27 On the visualization of processes Introducing the new local view: the rationale P. 27
28 On the visualization of constraints The static local view: some examples P. 28
29 On the representation of processes The static global view Basic Extended P. 29
30 A GUI sketch Local and global views together P. 30
31 On the representation of constraints Dynamic view P. 31
32 Open challenges Your contribution is welcome Event frequency handling in MINERful Error injection and robustness testing/improving Auto-thresholding Definition of a basis for declarative processes Graphical model for declarative processes in MailOfMine Implementation and usability testing Auto-refactoring of the dynamic view Refactoring in case of user-driven deviations from the process model P. 32
33 References Cited articles and resources, in order of appearance [Aalst2011.book] van der Aalst, W.M.P.: : Discovery, Conformance and Enhancement of Business Processes. Springer (2011). [AalstEtAl2009] van der Aalst, W.M.P., van Dongen, B.F., Güther, C.W., Rozinat, A., Verbeek, E., Weijters, T.: Prom: The process mining toolkit. In de Medeiros, A.K.A., Weber, B., eds.: BPM (Demos). Volume 489 of CEUR Workshop Proceedings., CEUR-WS.org (2009) [AalstEtAl2004] van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9) (2004) [WenEtAl2007] Wen, L., van der Aalst, W.M.P., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Discov. 15(2) (2007) [GüntherEtAl2007] Günther, C.W., van der Aalst, W.M.P.: Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics. BPM 2007: [WeijtersEtAl2001] Weijters, A., van der Aalst, W.: Rediscovering workflow models from event-based data using little thumb. Integrated Computer-Aided Engineering 10 (2001) [MedeirosEtAl2007] Medeiros, A.K., Weijters, A.J., Aalst, W.M.: Genetic process mining: an experi- mental evaluation. Data Min. Knowl. Discov. 14(2) (2007) [AalstEtAl2010] van der Aalst, W., Rubin, V., Verbeek, H., van Dongen, B., Kindler, E., Gnther, C.: Process mining: a two-step approach to balance between underfitting and overfitting. Software and Systems Modeling 9 (2010) /s z. P. 33
34 References Cited articles and resources, in order of appearance [HillEtAl06] Hill, C., Yates, R., Jones, C., Kogan, S.L.: Beyond predictable workflows: Enhancing productivity in artful business processes. IBM Systems Journal 45(4), (2006) [ACTIVE09] Warren, P., Kings, N., et al.: Improving knowledge worker productivity - the active integrated approach. BT Technology Journal 26(2), (2009) [DiCiccioEtAl11] Di Ciccio, C., Mecella, M., Catarci, T.: Representing and Visualizing Mined Artful Processes in MailOfMine. USAB 2011:83-94 [DiCiccioMecella12] Di Ciccio, C., Mecella,M.: Mining constraints for artful processes. In W. Abramowicz, D. Kriksciuniene, V.S., ed.: 15th International Conference on Business Information Systems. Volume 117 of Lecture Notes in Business Information Processing., Springer (2012) (to appear). [DiCiccioMecella/TR12] Di Ciccio, C., Mecella, M.: MINERful, a mining algorithm for declarative process constraints in MailOfMine. Technical report, Dipartimento di Ingegneria Infor- matica, Automatica e Gestionale Antonio Ruberti SAPIENZA, Universita` di Roma (2012). [AalstEtAl06] van der Aalst, W.M.P., Pesic, M.: Decserflow: Towards a truly declarative service flow language. Proc. WS-FM 2006 [MaggiEtAl11] Maggi, F.M., Mooij, A.J., van der Aalst, W.M.P.: User-guided discovery of declar- ative process models. In: CIDM, IEEE (2011) [Pnueli77] Pnueli, A.: The Temporal Logic of Programs. Proc. 18th Annual Symposium on Foundations of Software Technology and Theoretical Computer Science, 1977 P. 34
Process Mining An index to the state of the art and an outline of open research challenges at DIAG
An index to the state of the art and an outline of open research challenges at DIAG Claudio Di Ciccio, Massimo Mecella Seminars in Software and Services for the Information Society Definition [Aalst2011.book],
More informationKnowledge-intensive Processes: An Overview of Contemporary Approaches Claudio Di Ciccio, Andrea Marrella and Alessandro Russo
Knowledge-intensive Processes: An Overview of Contemporary Approaches Claudio Di Ciccio, Andrea Marrella and Alessandro Russo Claudio Di Ciccio (cdc@dis.uniroma1.it) 1 st International Workshop on Knowledge-intensive
More informationFeature. Applications of Business Process Analytics and Mining for Internal Control. World
Feature Filip Caron is a doctoral researcher in the Department of Decision Sciences and Information Management, Information Systems Group, at the Katholieke Universiteit Leuven (Flanders, Belgium). Jan
More informationBusiness Process Modeling
Business Process Concepts Process Mining Kelly Rosa Braghetto Instituto de Matemática e Estatística Universidade de São Paulo kellyrb@ime.usp.br January 30, 2009 1 / 41 Business Process Concepts Process
More informationCHAPTER 1 INTRODUCTION
CHAPTER 1 INTRODUCTION 1.1 Research Motivation In today s modern digital environment with or without our notice we are leaving our digital footprints in various data repositories through our daily activities,
More informationDotted Chart and Control-Flow Analysis for a Loan Application Process
Dotted Chart and Control-Flow Analysis for a Loan Application Process Thomas Molka 1,2, Wasif Gilani 1 and Xiao-Jun Zeng 2 Business Intelligence Practice, SAP Research, Belfast, UK The University of Manchester,
More informationEfficient Discovery of Understandable Declarative Process Models from Event Logs
Efficient Discovery of Understandable Declarative Process Models from Event Logs Fabrizio M. Maggi, R.P. Jagadeesh Chandra Bose, and Wil M.P. van der Aalst Eindhoven University of Technology, The Netherlands.
More informationProcess Mining Online Assessment Data
Process Mining Online Assessment Data Mykola Pechenizkiy, Nikola Trčka, Ekaterina Vasilyeva, Wil van der Aalst, Paul De Bra {m.pechenizkiy, e.vasilyeva, n.trcka, w.m.p.v.d.aalst}@tue.nl, debra@win.tue.nl
More informationModel Discovery from Motor Claim Process Using Process Mining Technique
International Journal of Scientific and Research Publications, Volume 3, Issue 1, January 2013 1 Model Discovery from Motor Claim Process Using Process Mining Technique P.V.Kumaraguru *, Dr.S.P.Rajagopalan
More informationReplaying History. prof.dr.ir. Wil van der Aalst www.processmining.org
Replaying History prof.dr.ir. Wil van der Aalst www.processmining.org Growth of data PAGE 1 Process Mining: Linking events to models PAGE 2 Where did we apply process mining? Municipalities (e.g., Alkmaar,
More informationHandling Big(ger) Logs: Connecting ProM 6 to Apache Hadoop
Handling Big(ger) Logs: Connecting ProM 6 to Apache Hadoop Sergio Hernández 1, S.J. van Zelst 2, Joaquín Ezpeleta 1, and Wil M.P. van der Aalst 2 1 Department of Computer Science and Systems Engineering
More informationAnalysis of Service Level Agreements using Process Mining techniques
Analysis of Service Level Agreements using Process Mining techniques CHRISTIAN MAGER University of Applied Sciences Wuerzburg-Schweinfurt Process Mining offers powerful methods to extract knowledge from
More informationREFlex: an entire solution to business process modeling
REFlex: an entire solution to business process modeling Renata M. de Carvalho and Natália C. Silva 1 University of Quebec at Montreal, LATECE Laboratory, Canada, renatawm@gmail.com 2 C.E.S.A.R - Recife
More informationMercy Health System. St. Louis, MO. Process Mining of Clinical Workflows for Quality and Process Improvement
Mercy Health System St. Louis, MO Process Mining of Clinical Workflows for Quality and Process Improvement Paul Helmering, Executive Director, Enterprise Architecture Pete Harrison, Data Analyst, Mercy
More informationProcess Mining and Monitoring Processes and Services: Workshop Report
Process Mining and Monitoring Processes and Services: Workshop Report Wil van der Aalst (editor) Eindhoven University of Technology, P.O.Box 513, NL-5600 MB, Eindhoven, The Netherlands. w.m.p.v.d.aalst@tm.tue.nl
More informationProM 6 Tutorial. H.M.W. (Eric) Verbeek mailto:h.m.w.verbeek@tue.nl R. P. Jagadeesh Chandra Bose mailto:j.c.b.rantham.prabhakara@tue.
ProM 6 Tutorial H.M.W. (Eric) Verbeek mailto:h.m.w.verbeek@tue.nl R. P. Jagadeesh Chandra Bose mailto:j.c.b.rantham.prabhakara@tue.nl August 2010 1 Introduction This document shows how to use ProM 6 to
More informationProcess Mining Framework for Software Processes
Process Mining Framework for Software Processes Vladimir Rubin 1,2, Christian W. Günther 1, Wil M.P. van der Aalst 1, Ekkart Kindler 2, Boudewijn F. van Dongen 1, and Wilhelm Schäfer 2 1 Eindhoven University
More informationConfiguring IBM WebSphere Monitor for Process Mining
Configuring IBM WebSphere Monitor for Process Mining H.M.W. Verbeek and W.M.P. van der Aalst Technische Universiteit Eindhoven Department of Mathematics and Computer Science P.O. Box 513, 5600 MB Eindhoven,
More informationProcess Modelling from Insurance Event Log
Process Modelling from Insurance Event Log P.V. Kumaraguru Research scholar, Dr.M.G.R Educational and Research Institute University Chennai- 600 095 India Dr. S.P. Rajagopalan Professor Emeritus, Dr. M.G.R
More informationConformance Checking of RBAC Policies in Process-Aware Information Systems
Conformance Checking of RBAC Policies in Process-Aware Information Systems Anne Baumgrass 1, Thomas Baier 2, Jan Mendling 2, and Mark Strembeck 1 1 Institute of Information Systems and New Media Vienna
More informationRelational XES: Data Management for Process Mining
Relational XES: Data Management for Process Mining B.F. van Dongen and Sh. Shabani Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands. B.F.v.Dongen, S.Shabaninejad@tue.nl
More informationProcess Mining A Comparative Study
International Journal of Advanced Research in Computer Communication Engineering Process Mining A Comparative Study Asst. Prof. Esmita.P. Gupta M.E. Student, Department of Information Technology, VIT,
More informationGeneration of a Set of Event Logs with Noise
Generation of a Set of Event Logs with Noise Ivan Shugurov International Laboratory of Process-Aware Information Systems National Research University Higher School of Economics 33 Kirpichnaya Str., Moscow,
More informationModeling and Analysis of Incoming Raw Materials Business Process: A Process Mining Approach
Modeling and Analysis of Incoming Raw Materials Business Process: A Process Mining Approach Mahendrawathi Er*, Hanim Maria Astuti, Dita Pramitasari Information Systems Department, Faculty of Information
More informationTowards a Software Framework for Automatic Business Process Redesign Marwa M.Essam 1, Selma Limam Mansar 2 1
ACEEE Int. J. on Communication, Vol. 02, No. 03, Nov 2011 Towards a Software Framework for Automatic Business Process Redesign Marwa M.Essam 1, Selma Limam Mansar 2 1 Faculty of Information and Computer
More informationActivity Mining for Discovering Software Process Models
Activity Mining for Discovering Software Process Models Ekkart Kindler, Vladimir Rubin, Wilhelm Schäfer Software Engineering Group, University of Paderborn, Germany [kindler, vroubine, wilhelm]@uni-paderborn.de
More informationUsing Semantic Lifting for improving Process Mining: a Data Loss Prevention System case study
Using Semantic Lifting for improving Process Mining: a Data Loss Prevention System case study Antonia Azzini, Chiara Braghin, Ernesto Damiani, Francesco Zavatarelli Dipartimento di Informatica Università
More informationTrace Clustering in Process Mining
Trace Clustering in Process Mining M. Song, C.W. Günther, and W.M.P. van der Aalst Eindhoven University of Technology P.O.Box 513, NL-5600 MB, Eindhoven, The Netherlands. {m.s.song,c.w.gunther,w.m.p.v.d.aalst}@tue.nl
More informationDiscovering User Communities in Large Event Logs
Discovering User Communities in Large Event Logs Diogo R. Ferreira, Cláudia Alves IST Technical University of Lisbon, Portugal {diogo.ferreira,claudia.alves}@ist.utl.pt Abstract. The organizational perspective
More informationAligning Event Logs and Declarative Process Models for Conformance Checking
Aligning Event Logs and Declarative Process Models for Conformance Checking Massimiliano de Leoni, Fabrizio M. Maggi, and Wil M. P. van der Aalst Eindhoven University of Technology, Eindhoven, The Netherlands
More informationThe ProM framework: A new era in process mining tool support
The ProM framework: A new era in process mining tool support B.F. van Dongen, A.K.A. de Medeiros, H.M.W. Verbeek, A.J.M.M. Weijters, and W.M.P. van der Aalst Department of Technology Management, Eindhoven
More informationProcess Mining. ^J Springer. Discovery, Conformance and Enhancement of Business Processes. Wil M.R van der Aalst Q UNIVERS1TAT.
Wil M.R van der Aalst Process Mining Discovery, Conformance and Enhancement of Business Processes Q UNIVERS1TAT m LIECHTENSTEIN Bibliothek ^J Springer Contents 1 Introduction I 1.1 Data Explosion I 1.2
More informationCombination of Process Mining and Simulation Techniques for Business Process Redesign: A Methodological Approach
Combination of Process Mining and Simulation Techniques for Business Process Redesign: A Methodological Approach Santiago Aguirre, Carlos Parra, and Jorge Alvarado Industrial Engineering Department, Pontificia
More informationTowards an Evaluation Framework for Process Mining Algorithms
Towards an Evaluation Framework for Process Mining Algorithms A. Rozinat, A.K. Alves de Medeiros, C.W. Günther, A.J.M.M. Weijters, and W.M.P. van der Aalst Eindhoven University of Technology P.O. Box 513,
More informationEFFECTIVE CONSTRUCTIVE MODELS OF IMPLICIT SELECTION IN BUSINESS PROCESSES. Nataliya Golyan, Vera Golyan, Olga Kalynychenko
380 International Journal Information Theories and Applications, Vol. 18, Number 4, 2011 EFFECTIVE CONSTRUCTIVE MODELS OF IMPLICIT SELECTION IN BUSINESS PROCESSES Nataliya Golyan, Vera Golyan, Olga Kalynychenko
More informationSeparating Compliance Management and Business Process Management
Separating Compliance Management and Business Process Management Elham Ramezani 1, Dirk Fahland 2, Jan Martijn van der Werf 2, and Peter Mattheis 1 1 Hochschule Furtwangen, Germany (ramezani Peter.Mattheis)@hs-furtwangen.de
More informationFormal Modeling and Analysis by Simulation of Data Paths in Digital Document Printers
Formal Modeling and Analysis by Simulation of Data Paths in Digital Document Printers Venkatesh Kannan, Wil M.P. van der Aalst, and Marc Voorhoeve Department of Mathematics and Computer Science, Eindhoven
More informationApplication of Process Mining in Healthcare A Case Study in a Dutch Hospital
Application of Process Mining in Healthcare A Case Study in a Dutch Hospital R.S. Mans 1, M.H. Schonenberg 1, M. Song 1, W.M.P. van der Aalst 1, and P.J.M. Bakker 2 1 Department of Information Systems
More informationProcess mining challenges in hospital information systems
Proceedings of the Federated Conference on Computer Science and Information Systems pp. 1135 1140 ISBN 978-83-60810-51-4 Process mining challenges in hospital information systems Payam Homayounfar Wrocław
More informationCPN Tools 4: A Process Modeling Tool Combining Declarative and Imperative Paradigms
CPN Tools 4: A Process Modeling Tool Combining Declarative and Imperative Paradigms Michael Westergaard 1,2 and Tijs Slaats 3,4 1 Department of Mathematics and Computer Science, Eindhoven University of
More informationBPIC 2014: Insights from the Analysis of Rabobank Service Desk Processes
BPIC 2014: Insights from the Analysis of Rabobank Service Desk Processes Bruna Christina P. Brandão, Guilherme Neves Lopes, Pedro Henrique P. Richetti Department of Applied Informatics - Federal University
More informationArticle. Abstract. This is a pre-print version. For the printed version please refer to www.wisu.de
Article StB Prof. Dr. Nick Gehrke Nordakademie Chair for Information Systems Köllner Chaussee 11 D-25337 Elmshorn nick.gehrke@nordakademie.de Michael Werner, Dipl.-Wirt.-Inf. University of Hamburg Chair
More informationProM Framework Tutorial
ProM Framework Tutorial Authors: Ana Karla Alves de Medeiros (a.k.medeiros@.tue.nl) A.J.M.M. (Ton) Weijters (a.j.m.m.weijters@tue.nl) Technische Universiteit Eindhoven Eindhoven, The Netherlands February
More informationTowards Cross-Organizational Process Mining in Collections of Process Models and their Executions
Towards Cross-Organizational Process Mining in Collections of Process Models and their Executions J.C.A.M. Buijs, B.F. van Dongen, W.M.P. van der Aalst Department of Mathematics and Computer Science, Eindhoven
More informationOnline Compliance Monitoring of Service Landscapes
Online Compliance Monitoring of Service Landscapes J.M.E.M. van der Werf 1 and H.M.W. Verbeek 2 1 Department of Information and Computing Science, Utrecht University, The Netherlands J.M.E.M.vanderWerf@UU.nl
More informationThe Research on the Usage of Business Process Mining in the Implementation of BPR
2007 IFIP International Conference on Network and Parallel Computing - Workshops The Research on Usage of Business Process Mining in Implementation of BPR XIE Yi wu 1, LI Xiao wan 1, Chen Yan 2 (1.School
More informationOn the Modeling and Verification of Security-Aware and Process-Aware Information Systems
On the Modeling and Verification of Security-Aware and Process-Aware Information Systems 29 August 2011 What are workflows to us? Plans or schedules that map users or resources to tasks Such mappings may
More informationHow To Find The Model Of A Process From The Run Time
Discovering Process Models from Unlabelled Event Logs Diogo R. Ferreira 1 and Daniel Gillblad 2 1 IST Technical University of Lisbon 2 Swedish Institute of Computer Science (SICS) diogo.ferreira@ist.utl.pt,
More informationUsing Process Mining to Bridge the Gap between BI and BPM
Using Process Mining to Bridge the Gap between BI and BPM Wil van der alst Eindhoven University of Technology, The Netherlands Process mining techniques enable process-centric analytics through automated
More informationWorkflow Management Models and ConDec
A Declarative Approach for Flexible Business Processes Management M. Pesic and W.M.P. van der Aalst Department of Technology Management, Eindhoven University of Technology, P.O.Box 513, NL-5600 MB, Eindhoven,
More informationDiscovering Data-Aware Declarative Process Models from Event Logs
Discovering Data-Aware Declarative Process Models from Event Logs Fabrizio M. Maggi 1, Marlon Dumas 1, Luciano García-añuelos 1, and Marco Montali 2 1 University of Tartu, Estonia {f.m.maggi, marlon.dumas,
More informationProcess Mining for Electronic Data Interchange
Process Mining for Electronic Data Interchange R. Engel 1, W. Krathu 1, C. Pichler 2, W. M. P. van der Aalst 3, H. Werthner 1, and M. Zapletal 1 1 Vienna University of Technology, Austria Institute for
More informationA Research Article on Data Mining in Addition to Process Mining: Similarities and Dissimilarities
A Research Article on Data Mining in Addition to Process Mining: Similarities and Dissimilarities S. Sowjanya Chintalapati 1, Ch.G.V.N.Prasad 2, J. Sowjanya 3, R.Vineela 4 1, 3, 4 Assistant Professor,
More informationPLG: a Framework for the Generation of Business Process Models and their Execution Logs
PLG: a Framework for the Generation of Business Process Models and their Execution Logs Andrea Burattin and Alessandro Sperduti Department of Pure and Applied Mathematics University of Padua, Italy {burattin,sperduti}@math.unipd.it
More informationBPMN PATTERNS USED IN MANAGEMENT INFORMATION SYSTEMS
BPMN PATTERNS USED IN MANAGEMENT INFORMATION SYSTEMS Gabriel Cozgarea 1 Adrian Cozgarea 2 ABSTRACT: Business Process Modeling Notation (BPMN) is a graphical standard in which controls and activities can
More informationAn Outlook on Semantic Business Process Mining and Monitoring
An Outlook on Semantic Business Process Mining and Monitoring A.K. Alves de Medeiros 1,C.Pedrinaci 2, W.M.P. van der Aalst 1, J. Domingue 2,M.Song 1,A.Rozinat 1,B.Norton 2, and L. Cabral 2 1 Eindhoven
More informationDecision Mining in Business Processes
Decision Mining in Business Processes A. Rozinat and W.M.P. van der Aalst Department of Technology Management, Eindhoven University of Technology P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands {a.rozinat,w.m.p.v.d.aalst}@tm.tue.nl
More informationLluis Belanche + Alfredo Vellido. Intelligent Data Analysis and Data Mining. Data Analysis and Knowledge Discovery
Lluis Belanche + Alfredo Vellido Intelligent Data Analysis and Data Mining or Data Analysis and Knowledge Discovery a.k.a. Data Mining II An insider s view Geoff Holmes: WEKA founder Process Mining
More informationChapter 12 Analyzing Spaghetti Processes
Chapter 12 Analyzing Spaghetti Processes prof.dr.ir. Wil van der Aalst www.processmining.org Overview Chapter 1 Introduction Part I: Preliminaries Chapter 2 Process Modeling and Analysis Chapter 3 Data
More informationImplementing Heuristic Miner for Different Types of Event Logs
Implementing Heuristic Miner for Different Types of Event Logs Angelina Prima Kurniati 1, GunturPrabawa Kusuma 2, GedeAgungAry Wisudiawan 3 1,3 School of Compuing, Telkom University, Indonesia. 2 School
More informationBusiness Process Measurement in small enterprises after the installation of an ERP software.
Business Process Measurement in small enterprises after the installation of an ERP software. Stefano Siccardi and Claudia Sebastiani CQ Creativiquadrati snc, via Tadino 60, Milano, Italy http://www.creativiquadrati.it
More informationProcess Mining in Big Data Scenario
Process Mining in Big Data Scenario Antonia Azzini, Ernesto Damiani SESAR Lab - Dipartimento di Informatica Università degli Studi di Milano, Italy antonia.azzini,ernesto.damiani@unimi.it Abstract. In
More informationTilburg University. Publication date: 2010. Link to publication
Tilburg University On the formal specification of business contracts and regulatory compliance Elgammal, Onbekend; Türetken, O.; van den Heuvel, Willem-Jan; Papazoglou, Mike Published in: Proceedings of
More informationCCaaS: Online Conformance Checking as a Service
CCaaS: Online Conformance Checking as a Service Ingo Weber 1, Andreas Rogge-Solti 2, Chao Li 1, and Jan Mendling 2 1 NICTA, Sydney, Australia firstname.lastname@nicta.com.au 2 Wirtschaftsuniversität Wien,
More informationProcess Mining and Fraud Detection
Process Mining and Fraud Detection A case study on the theoretical and practical value of using process mining for the detection of fraudulent behavior in the procurement process Masters of Science Thesis
More informationBUsiness process mining, or process mining in a short
, July 2-4, 2014, London, U.K. A Process Mining Approach in Software Development and Testing Process: A Case Study Rabia Saylam, Ozgur Koray Sahingoz Abstract Process mining is a relatively new and emerging
More informationERP Event Log Preprocessing: Timestamps vs. Accounting Logic
ERP Event Log Preprocessing: Timestamps vs. Accounting Logic Niels Mueller-Wickop and Martin Schultz Chair for Information Systems, University of Hamburg, Hamburg, Germany {niels.mueller-wickop,martin.schultz}@wiso.uni-hamburg.de
More informationImproving Business Process Models with Agent-based Simulation and Process Mining
Improving Business Process Models with Agent-based Simulation and Process Mining Fernando Szimanski 1, Célia G. Ralha 1, Gerd Wagner 2, and Diogo R. Ferreira 3 1 University of Brasília, Brazil fszimanski@gmail.com,
More informationTranslating Message Sequence Charts to other Process Languages using Process Mining
Translating Message Sequence Charts to other Process Languages using Process Mining Kristian Bisgaard Lassen 1, Boudewijn F. van Dongen 2, and Wil M.P. van der Aalst 2 1 Department of Computer Science,
More informationB. Majeed British Telecom, Computational Intelligence Group, Ipswich, UK
The current issue and full text archive of this journal is available at wwwemeraldinsightcom/1463-7154htm A review of business process mining: state-of-the-art and future trends A Tiwari and CJ Turner
More informationVerifying Business Processes Extracted from E-Commerce Systems Using Dynamic Analysis
Verifying Business Processes Extracted from E-Commerce Systems Using Dynamic Analysis Derek Foo 1, Jin Guo 2 and Ying Zou 1 Department of Electrical and Computer Engineering 1 School of Computing 2 Queen
More informationEMiT: A process mining tool
EMiT: A process mining tool B.F. van Dongen and W.M.P. van der Aalst Department of Technology Management, Eindhoven University of Technology P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands. b.f.v.dongen@tue.nl
More informationSummary and Outlook. Business Process Intelligence Course Lecture 8. prof.dr.ir. Wil van der Aalst. www.processmining.org
Business Process Intelligence Course Lecture 8 Summary and Outlook prof.dr.ir. Wil van der Aalst www.processmining.org Overview Chapter 1 Introduction Part I: Preliminaries Chapter 2 Process Modeling and
More informationAn Ontology-based Framework for Enriching Event-log Data
An Ontology-based Framework for Enriching Event-log Data Thanh Tran Thi Kim, Hannes Werthner e-commerce group Institute of Software Technology and Interactive Systems, Vienna, Austria Email: kimthanh@ec.tuwien.ac.at,
More informationCompliance Analysis in IT Service Management Systems. Master Thesis
Compliance Analysis in IT Service Management Systems Master Thesis IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE (M.Sc.) IN INFORMATION SYSTEMS AT THE SCHOOL OF BUSINESS
More informationThe Open University s repository of research publications and other research outputs
Open Research Online The Open University s repository of research publications and other research outputs Semantic process mining tools: core building blocks Conference Item How to cite: de Medeiros, Ana
More informationThe Roman Model for Automated Synthesis in Practice: the SM4All Experience
The Roman Model for Automated Synthesis in Practice: the SM4All Experience An implementation of the game structure based automated syntesis of services applied to a real scenario Mario Caruso 1 Claudio
More informationBIS 3106: Business Process Management. Lecture Two: Modelling the Control-flow Perspective
BIS 3106: Business Process Management Lecture Two: Modelling the Control-flow Perspective Makerere University School of Computing and Informatics Technology Department of Computer Science SEM I 2015/2016
More informationBusiness Process Improvement Framework and Representational Support
Business Process Improvement Framework and Representational Support Azeem Lodhi, Veit Köppen, and Gunter Saake Department of Technical and Business Information Systems, Faculty of Computer Science, University
More informationMaster Thesis September 2010 ALGORITHMS FOR PROCESS CONFORMANCE AND PROCESS REFINEMENT
Master in Computing Llenguatges i Sistemes Informàtics Master Thesis September 2010 ALGORITHMS FOR PROCESS CONFORMANCE AND PROCESS REFINEMENT Student: Advisor/Director: Jorge Muñoz-Gama Josep Carmona Vargas
More informationSOFTWARE PROCESS MINING
SOFTWARE PROCESS MINING DR. VLADIMIR RUBIN LEAD IT ARCHITECT & CONSULTANT @ DR. RUBIN IT CONSULTING LEAD RESEARCH FELLOW @ PAIS LAB / HSE ANNOTATION Nowadays, in the era of social, mobile and cloud computing,
More informationMining Configurable Process Models from Collections of Event Logs
Mining Configurable Models from Collections of Event Logs J.C.A.M. Buijs, B.F. van Dongen, and W.M.P. van der Aalst Eindhoven University of Technology, The Netherlands {j.c.a.m.buijs,b.f.v.dongen,w.m.p.v.d.aalst}@tue.nl
More informationThe Resultmaker Online Consultant: From Declarative Workflow Management in Practice to LTL
The Resultmaker Online Consultant: From Declarative Workflow Management in Practice to LTL Mukkamala Raghava Rao Industrial PhD student IT University of Copenhagen & Resultmaker A/S rao@resultmaker.com
More informationDept. of IT in Vel Tech Dr. RR & Dr. SR Technical University, INDIA. Dept. of Computer Science, Rashtriya Sanskrit Vidyapeetha, INDIA
ISSN : 2229-4333(Print) ISSN : 0976-8491(Online) Analysis of Workflow and Process Mining in Dyeing Processing System 1 Saravanan. M.S, 2 Dr Rama Sree.R.J 1 Dept. of IT in Vel Tech Dr. RR & Dr. SR Technical
More informationA Software Framework for Risk-Aware Business Process Management
A Software Framework for Risk-Aware Business Management Raffaele Conforti 1, Marcello La Rosa 1,2, Arthur H.M. ter Hofstede 1,4, Giancarlo Fortino 3, Massimiliano de Leoni 4, Wil M.P. van der Aalst 4,1,
More information08 BPMN/1. Software Technology 2. MSc in Communication Sciences 2009-10 Program in Technologies for Human Communication Davide Eynard
MSc in Communication Sciences 2009-10 Program in Technologies for Human Communication Davide Eynard Software Technology 2 08 BPMN/1 2 ntro Sequence of (three?) lessons on BPMN and technologies related
More informationFrom Workflow Design Patterns to Logical Specifications
AUTOMATYKA/ AUTOMATICS 2013 Vol. 17 No. 1 http://dx.doi.org/10.7494/automat.2013.17.1.59 Rados³aw Klimek* From Workflow Design Patterns to Logical Specifications 1. Introduction Formal methods in software
More informationManaging and Tracing the Traversal of Process Clouds with Templates, Agendas and Artifacts
Managing and Tracing the Traversal of Process Clouds with Templates, Agendas and Artifacts Marian Benner, Matthias Book, Tobias Brückmann, Volker Gruhn, Thomas Richter, Sema Seyhan paluno The Ruhr Institute
More informationOn Global Completeness of Event Logs
On Global Completeness of Event Logs Hedong Yang 1, Arthur HM ter Hofstede 2,3, B.F. van Dongen 3, Moe T. Wynn 2, and Jianmin Wang 1 1 Tsinghua University, Beijing, China, 10084 yanghd06@mails.tsinghua.edu.cn,jimwang@tsinghua.edu.cn
More informationBusiness Process Mining: From Theory to Practice
Abstract Business Process Mining: From Theory to Practice C.J. Turner, A. Tiwari, R. A. Olaiya and Y, Xu Purpose - This paper presents a comparison of a number of business process mining tools currently
More informationConformance Checking of Interacting Processes With Overlapping Instances
Conformance Checking of Interacting Processes With Overlapping Instances Dirk Fahland, Massimiliano de Leoni, Boudewijn F. van Dongen, and Wil M.P. van der Aalst Eindhoven University of Technology, The
More informationProcess Mining: Making Knowledge Discovery Process Centric
Process Mining: Making Knowledge Discovery Process Centric Wil van der alst Department of Mathematics and Computer Science Eindhoven University of Technology PO Box 513, 5600 MB, Eindhoven, The Netherlands
More informationA Recommender System for Process Discovery
A Recommender System for Process Discovery Joel Ribeiro 1, Josep Carmona 1, Mustafa Mısır 2, and Michele Sebag 2 1 Universitat Politècnica de Catalunya, Spain. {jribeiro, jcarmona}@lsi.upc.edu 2 TAO, INRIA
More informationGenet A tool for the synthesis and mining of Petri nets. Josep Carmona jcarmonalsi.upc.edu Software Department Universitat Politcnica de Catalunya
Genet A tool for the synthesis and mining of Petri nets Josep Carmona jcarmonalsi.upc.edu Software Department Universitat Politcnica de Catalunya 2 Contents 1.1 Overview of the tool.......................
More informationEventifier: Extracting Process Execution Logs from Operational Databases
Eventifier: Extracting Process Execution Logs from Operational Databases Carlos Rodríguez 1, Robert Engel 2, Galena Kostoska 1, Florian Daniel 1, Fabio Casati 1, and Marco Aimar 3 1 University of Trento,
More informationA Framework of User-Driven Data Analytics in the Cloud for Course Management
A Framework of User-Driven Data Analytics in the Cloud for Course Management Jie ZHANG 1, William Chandra TJHI 2, Bu Sung LEE 1, Kee Khoon LEE 2, Julita VASSILEVA 3 & Chee Kit LOOI 4 1 School of Computer
More informationBidimensional Process Discovery for Mining BPMN Models
Bidimensional Process Discovery for Mining BPMN Models Jochen De Weerdt, Seppe K.L.M. vanden Broucke and Filip Caron Department of Decision Sciences and Information Management, KU Leuven Naamsestraat 69,
More informationProcess Mining. Data science in action
Process Mining. Data science in action Julia Rudnitckaia Brno, University of Technology, Faculty of Information Technology, irudnickaia@fit.vutbr.cz 1 Abstract. At last decades people have to accumulate
More informationLearning Business Rules for Adaptive Process Models
Learning Business Rules for Adaptive Process Models Hans Friedrich Witschel, Tuan Q. Nguyen, Knut Hinkelmann Fachhochschule Nordwestschweiz FHNW Olten, Switzerland hansfriedrich.witschel@fhnw.ch, nguyen.quoctuan@students.fhnw.ch,
More informationDesigning and Evaluating an Interpretable Predictive Modeling Technique for Business Processes
Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes Dominic Breuker 1, Patrick Delfmann 1, Martin Matzner 1 and Jörg Becker 1 1 Department for Information Systems,
More information