Process Trace Clustering: A Heterogeneous Information Network Approach

A computer-implemented method of generating process models from process event logs, including receiving an identification of node types and edge types of an application event log to generate a heterogeneous information network graph of the application event log, where node types include events and traces, where each trace is a finite sequence of event type nodes; reducing a number of event types of the set of input traces to generate clusters of new event types; and clustering the set of input traces to generate a plurality of disjoint partitions based on the clusters of new event types, where the clustering maximizes an average fitness of each partition and minimizes an average complexity of each partition, where each partition is a graph model of a process in the application event log.

[1]  Wil M. P. van der Aalst,et al.  Conformance checking of processes based on monitoring real behavior , 2008, Inf. Syst..

[2]  Gonzalo Navarro,et al.  A guided tour to approximate string matching , 2001, CSUR.

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

[4]  Philip S. Yu,et al.  Integrating meta-path selection with user-guided object clustering in heterogeneous information networks , 2012, KDD.

[5]  Bart Baesens,et al.  Active Trace Clustering for Improved Process Discovery , 2013, IEEE Transactions on Knowledge and Data Engineering.

[6]  Luigi Pontieri,et al.  Discovering expressive process models by clustering log traces , 2006, IEEE Transactions on Knowledge and Data Engineering.

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

[8]  Jianmin Wang,et al.  Mining process models with non-free-choice constructs , 2007, Data Mining and Knowledge Discovery.

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

[10]  Mykola Pechenizkiy,et al.  A comparative study of dimensionality reduction techniques to enhance trace clustering performances , 2013, Expert Syst. Appl..

[11]  Wil M. P. van der Aalst,et al.  The Application of Petri Nets to Workflow Management , 1998, J. Circuits Syst. Comput..

[12]  Wil M. P. van der Aalst,et al.  Trace Clustering in Process Mining , 2008, Business Process Management Workshops.

[13]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[14]  Peter Kulchyski.  and , 2015 .

[15]  Activité ad‐hoc Types de tâches.  Introduction to BPMN , 2004 .

[16]  Boudewijn F. van Dongen,et al.  Process Mining Based on Clustering: A Quest for Precision , 2007, Business Process Management Workshops.

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

[18]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[19]  Boudewijn F. van Dongen,et al.  The ProM Framework: A New Era in Process Mining Tool Support , 2005, ICATPN.

[20]  Yizhou Sun,et al.  Mining heterogeneous information networks: a structural analysis approach , 2013, SKDD.

[21]  Wil M.P. van der Aalst,et al.  Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics , 2007, BPM.

[22]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[23]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..

[24]  I K Fodor,et al.  A Survey of Dimension Reduction Techniques , 2002 .

[25]  Wil M.P. van der Aalst,et al.  Process mining with the HeuristicsMiner algorithm , 2006 .