Process Mining Discovery, Conformance and Enhancement of Business Processes.PDF
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1、Process MiningWil M.P.van der AalstProcessMiningDiscovery,Conformance andEnhancement of Business ProcessesWil M.P.van der AalstDepartment Mathematics&Computer ScienceEindhoven University of TechnologyDen Dolech 25612 AZ EindhovenThe Netherlandsw.m.p.v.d.aalsttue.nlISBN 978-3-642-19344-6e-ISBN 978-3-
2、642-19345-3DOI 10.1007/978-3-642-19345-3Springer Heidelberg Dordrecht London New YorkLibrary of Congress Control Number:2011926240ACM Computing Classication(1998):H.4.1,H.2.8,I.2.6,F.3.2,D.2.2,J.1 Springer-Verlag Berlin Heidelberg 2011This work is subject to copyright.All rights are reserved,whether
3、 the whole or part of the material isconcerned,specically the rights of translation,reprinting,reuse of illustrations,recitation,broadcasting,reproduction on microlm or in any other way,and storage in data banks.Duplication of this publicationor parts thereof is permitted only under the provisions o
4、f the German Copyright Law of September 9,1965,in its current version,and permission for use must always be obtained from Springer.Violationsare liable to prosecution under the German Copyright Law.The use of general descriptive names,registered names,trademarks,etc.in this publication does notimply
5、,evenintheabsenceof aspecic statement,that suchnames areexempt from therelevantprotectivelaws and regulations and therefore free for general use.Cover design:deblikPrinted on acid-free paperSpringer is part of Springer Science+Business Media()Thanks to Karin for understanding thatscience is more rew
6、arding than runningerrandsThanks to all people that contributed toProM;the fruits of their efforts demonstratethat sharing a common goal is moremeaningful than“cashing in the nextpublon”1In remembrance of Gerry Straatman-Beelen(19322010)1publon=smallest publishable unitPrefaceProcess mining provides
7、 a new means to improve processes in a variety of applica-tion domains.There are two main drivers for this new technology.On the one hand,more and more events are being recorded thus providing detailed information aboutthe history of processes.Despite the omnipresence of event data,most organization
8、sdiagnose problems based on ction rather than facts.On the other hand,vendors ofBusiness Process Management(BPM)and Business Intelligence(BI)software havebeen promising miracles.Although BPM and BI technologies received lots of atten-tion,they did not live up to the expectations raised by academics,
9、consultants,andsoftware vendors.Process mining is an emerging discipline providing comprehensive sets of toolsto provide fact-based insights and to support process improvements.This new disci-pline buildson process model-drivenapproachesanddata mining.However,processmining is much more than an amalg
10、amation of existing approaches.For example,existing data mining techniques are too data-centric to provide a comprehensive un-derstanding of the end-to-end processes in an organization.BI tools focus on sim-ple dashboards and reporting rather than clear-cut business process insights.BPMsuites heavil
11、y rely on experts modeling idealized to-be processes and do not helpthe stakeholders to understand the as-is processes.This book presents a range of process mining techniques that help organizationsto uncover their actual business processes.Process mining is not limited to pro-cess discovery.By tigh
12、tly coupling event data and process models,it is possible tocheck conformance,detect deviations,predict delays,support decision making,andrecommend process redesigns.Process mining breathes life into otherwise staticprocess models and puts todays massive data volumes in a process context.Hence,manag
13、ements trends related to process improvement(e.g.,Six Sigma,TQM,CPI,and CPM)and compliance(SOX,BAM,etc.)can benet from process mining.Process mining,as described in this book,emerged in the last decade 102,106.However,the roots date back about half a century.For example,Anil Nerode pre-sented an app
14、roach to synthesize nite-state machines from example traces in 195871,Carl Adam Petri introduced the rst modeling language adequately capturingconcurrency in 1962 73,and Mark Gold was the rst to systematically exploreviiviiiPrefacedifferent notions of learnability in 1967 45.When data mining started
15、 to our-ish in the nineties,little attention was given to processes.Moreover,only recentlyevent logs have become omnipresent thus enabling end-to-end process discovery.Since the rst survey on process mining in 2003 102,progress has been spectacu-lar.Process mining techniques have become mature and s
16、upported by various tools.Moreover,whereas initially the primary focus was on process discovery,the pro-cess mining spectrum has broadened markedly.For instance,conformance check-ing,multi-perspectiveprocessmining,andoperationalsupporthave becomeintegralparts of ProM,one of the leading process minin
17、g tools.This is the rst book on process mining.Therefore,the intended audience isquite broad.The book provides a comprehensive overview of the state-of-the-artin process mining.It is intended as an introduction to the topic for practitioners,students,and academics.On the one hand,the book is accessi
18、ble for people that arenew to the topic.On the other hand,the book does not avoid explaining importantconcepts on a rigorous manner.The book aims to be self-contained while coveringthe entire process mining spectrum from process discovery to operational support.Therefore,it also serves as a referenc
19、e handbook for people dealing with BPM orBI on a day-to-day basis.The reader can immediately put process mining into practice due to the applica-bility of the techniques,the availability of(open-source)process mining software,and the abundance of event data in todays information systems.I sincerely
20、hopethat you enjoy reading this book and start using some of the amazing process min-ing techniques available today.Wil M.P.van der AalstSchleiden,GermanyDecember 2010AcknowledgementsMany individuals and organizations contributed to the techniques and tools de-scribed in this book.Therefore,it is im
21、portant to acknowledge their support,efforts,and contributions.All of this started in 1999 with a research project named“Process Design byDiscovery:Harvesting Workow Knowledge from Ad-hoc Executions”initiated byTon Weijters and myself.At that time,I was still working as a visiting professorat the Un
22、iversity of Colorado in Boulder.However,the research school BETA hadencouraged me to start collaborating with existing staff in my new research groupat TU/e(Eindhoven University of Technology).After talking to Ton it was clearthat we could benet from combining his knowledge of machine learning with
23、myknowledge of workow management and Petri nets.Process mining(at that time wecalled it workow mining)was the obvious topic for which we could combine ourexpertise.This was the start of a very successful collaboration.Thanks Ton!Since then many PhD students have been working on the topic:Laura Marus
24、ter,Ana Karla Alves de Medeiros,Boudewijn van Dongen,Minseok Song,Chris-tian Gnther,Anne Rozinat,Carmen Bratosin,R.P.Jagadeesh Chandra(JC)Bose,Ronny Mans,Maja Pesic,Joyce Nakatumba,Helen Schonenberg,Arya Adriansyah,and Joos Buijs.Im extremely grateful for their efforts.Ana Karla Alves de Medeiros wa
25、s the rst PhD student to work on the topicunder my supervision(genetic process mining).She did a wonderful job;her thesison genetic process mining was awarded with the prestigious ASML 2007 Promo-tion Prize and was selected as the best thesis by the KNAW research school BETA.Also Boudewijn van Donge
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