Detecting changes in a semi-structured business process through spectral graph analysis

Semi-structured business processes are emerging at a rapid pace in industries such as government, insurance, banking and healthcare. The workflows underlying these case-oriented processes are non-deterministic. They are mostly driven by human decision making and content status and they may change frequently depending on factors such as economic conditions, legislative policy changes and technological upgrades. This paper describes a method to detect changes in a running business process by conducting spectral graph analysis of sets of execution traces of the process. This method is beneficial because it does not require mining a process model of the business process, and is consequently independent of any assumptions about the nature of the business process. This makes it particularly applicable to case-oriented semi-structured business processes whose lifecycle is not fully driven by a formal process model. In this paper we present our algorithm for computing graph spectra from business process execution traces, and discuss some initial promising results, as well as exciting ideas generated by this research for future work.

[1]  Ekkart Kindler,et al.  Incremental Workflow Mining for Process Flexibility , 2006, BPMDS.

[2]  Jan Recker,et al.  Using process mining to learn from process changes in evolutionary systems , 2008, Int. J. Bus. Process. Integr. Manag..

[3]  Manfred Reichert,et al.  Discovering Reference Models by Mining Process Variants Using a Heuristic Approach , 2009, BPM.

[4]  Manfred Reichert,et al.  On Measuring Process Model Similarity Based on High-Level Change Operations , 2007, ER.

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

[6]  David M. Mount,et al.  Isomorphism of graphs with bounded eigenvalue multiplicity , 1982, STOC '82.

[7]  Emanuele Viola,et al.  Pseudorandom Bits for Polynomials , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[8]  Manfred Reichert,et al.  Adeptflex—Supporting Dynamic Changes of Workflows Without Losing Control , 1998, Journal of Intelligent Information Systems.

[9]  Yurdaer N. Doganata,et al.  Business Provenance - A Technology to Increase Traceability of End-to-End Operations , 2008, OTM Conferences.

[10]  Remco M. Dijkman,et al.  Measuring Similarity between Business Process Models , 2008, CAiSE.

[11]  Gregor Engels,et al.  Detecting and Resolving Process Model Differences in the Absence of a Change Log , 2008, BPM.

[12]  Liang-Jie Zhang,et al.  Development of Distance Measures for Process Mining, Discovery and Integration , 2007, Int. J. Web Serv. Res..

[13]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Daniel A. Spielman,et al.  Spectral Graph Theory and its Applications , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[15]  Remco M. Dijkman,et al.  Graph Matching Algorithms for Business Process Model Similarity Search , 2009, BPM.

[16]  Francisco Curbera,et al.  Predictive Analytics for Semi-structured Case Oriented Business Processes , 2010, Business Process Management Workshops.

[17]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.