Detecting Changes in Process Behavior Using Comparative Case Clustering

Real-life business processes are complex and often exhibit a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary, flexible business processes. This paper presents a novel comparative case clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation using both synthetic and real-life event data shows our technique can provide these insights.

[1]  van der Wmp Wil Aalst,et al.  Discovering deviating cases and process variants using trace clustering , 2015 .

[2]  Josep Carmona,et al.  Online Techniques for Dealing with Concept Drift in Process Mining , 2012, IDA.

[3]  Wil M. P. van der Aalst,et al.  Change Point Detection and Dealing with Gradual and Multi-order Dynamics in Process Mining , 2015, BIR.

[4]  Wil M. P. van der Aalst,et al.  Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models , 2009, Business Process Management Workshops.

[5]  Mykola Pechenizkiy,et al.  Handling Concept Drift in Process Mining , 2011, CAiSE.

[6]  Marlon Dumas,et al.  Fast and Accurate Business Process Drift Detection , 2015, BPM.

[7]  Diogo R. Ferreira,et al.  Understanding Spaghetti Models with Sequence Clustering for ProM , 2009, Business Process Management Workshops.

[8]  Jochen De Weerdt,et al.  Process discovery in event logs: An application in the telecom industry , 2011, Appl. Soft Comput..

[9]  Wil M. P. van der Aalst,et al.  Context Aware Trace Clustering: Towards Improving Process Mining Results , 2009, SDM.

[10]  Alessandro Sperduti,et al.  Online Discovery of Declarative Process Models from Event Streams , 2015, IEEE Transactions on Services Computing.

[11]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.

[12]  Seppe K. L. M. vanden Broucke,et al.  SECPI: Searching for Explanations for Clustered Process Instances , 2014, BPM.

[13]  Daniela Luengo,et al.  Applying Clustering in Process Mining to Find Different Versions of a Business Process That Changes over Time , 2011, Business Process Management Workshops.

[14]  Peter Loos,et al.  A Comparative Analysis of Process Instance Cluster Techniques , 2015, Wirtschaftsinformatik.

[15]  S. Dongen A cluster algorithm for graphs , 2000 .

[16]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..

[17]  Peter Tiño,et al.  Real-Time Detection of Process Change using Process Mining , 2011, ICCSW.

[18]  B. F. van Dongen BPI Challenge 2015 , 2015 .

[19]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .