Development of Distance Measures for Process Mining, Discovery and Integration

Business processes continue to play an important role in today’s service-oriented enterprise computing systems. Mining, discovering, and integrating process-oriented services has attracted growing attention in the recent years. In this article, we present a quantitative approach to modeling and capturing the similarity and dissimilarity between different process designs. We derive the similarity measures by analyzing the process dependency graphs of the participating workflow processes. We first convert each process dependency graph into a normalized process matrix. Then we calculate the metric space distance between the normalized matrices. This distance measure can be used as a quantitative and qualitative tool in process mining, process merging, and process clustering, and ultimately it can reduce or minimize the costs involved in design, analysis, and evolution of workflow systems.

[1]  Alexander L. Wolf,et al.  Software process validation: quantitatively measuring the correspondence of a process to a model , 1999, TSEM.

[2]  Dragos Manolescu,et al.  Production workflow: concepts and techniques , 2001, SOEN.

[3]  H. Anton,et al.  Elementary linear algebra : applications version , 2008 .

[4]  H. Anton.  Elementary Linear Algebra , 1970 .

[5]  Erich J. Neuhold,et al.  Matchmaking for Business Processes Based on Choreographies , 2004, Int. J. Web Serv. Res..

[6]  Horst Bunke,et al.  A graph distance metric based on the maximal common subgraph , 1998, Pattern Recognit. Lett..

[7]  Kaizhong Zhang,et al.  Simple Fast Algorithms for the Editing Distance Between Trees and Related Problems , 1989, SIAM J. Comput..

[8]  Guido Schimm,et al.  Mining exact models of concurrent workflows , 2004, Comput. Ind..

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

[10]  Hyerim Bae,et al.  Automatic control of workflow processes using ECA rules , 2004, IEEE Transactions on Knowledge and Data Engineering.

[11]  Kathleen M. Carley,et al.  Metric inference for social networks , 1994 .

[12]  Mohamed S. Kamel,et al.  Efficient phrase-based document indexing for Web document clustering , 2004, IEEE Transactions on Knowledge and Data Engineering.

[13]  Siu-Ming Yiu,et al.  An efficient and scalable algorithm for clustering XML documents by structure , 2004, IEEE Transactions on Knowledge and Data Engineering.

[14]  Byung-Hyun Ha,et al.  An Approximate Analysis of Expected Cycle Time in Business Process Execution , 2006, Business Process Management Workshops.

[15]  Dimitrios Gunopulos,et al.  Mining Process Models from Workflow Logs , 1998, EDBT.

[16]  H. G. Moore,et al.  Elementary linear algebra with applications , 1980 .

[17]  William A. Wallace,et al.  Elecitation of Knowledge from Multiple Experts Using Network Inference , 1997, IEEE Trans. Knowl. Data Eng..

[18]  Boudewijn F. van Dongen,et al.  Workflow mining: A survey of issues and approaches , 2003, Data Knowl. Eng..

[19]  William B. Rouse,et al.  A theory of enterprise transformation , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.