Hierarchical clustering of business process models

International Journal of Innovative Computing, Information and Control

Volumn 5, Issue 12, 2009, Pages 4501-4511

  Jung, Jae Yoon ^a   Bae, Joonsoo ^b   Liu, Ling ^c  

^a Kyung Hee University   (South Korea)

^b Chonbuk National University   (South Korea)

^c Georgia Institute of Technology   (United States)

Author keywords

Business process management (BPM); Hierarchical clustering; Workflow

Indexed keywords

BUSINESS PROCESS; BUSINESS PROCESS ANALYSIS; BUSINESS PROCESS MANAGEMENT; BUSINESS PROCESS MODEL; COMPETITIVE ADVANTAGE; HIER-ARCHICAL CLUSTERING; NEW PROCESS; PROCESS MODEL; PROCESS REENGINEERING; PROCESS-AWARE INFORMATION SYSTEMS; STRUCTURAL SIMILARITY; VALUE CREATION;

COMPETITION; ENTERPRISE RESOURCE MANAGEMENT; REENGINEERING;

PROCESS DESIGN;

EID: 74349091228     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. T. W. Malone, K. Crowston, and G. A. Herman, Organizing Business Knowledge: The MIT Process Handbook, The MIT Press, Cambridge, MA. 2003.
2. RosettaNet Implementation Framework: Core Specification 2.0, RosettaNet. http://www.rosettanet.org/.
3. W. M. P. van der Aalst and T. Basten, Inheritance of business processs: An approach to tackling problems related to change, Theoretical Computer Science, vol.270, no.1, pp.125-203, 2002.
4. W. M. P. van der Aalst and A. J. M. M. Weijters, Process mining: A research agenda, Computers in Industry, vol.53, no.3, pp.231-244, 2004.
5. G. Schimm, Mining exact models of concurrent workflows, Computers in Industry, vol.53, no.3, pp.265-281, 2004.
6. M. H. Jansen-Vullers, W. M. P. van der Aalst, and M. Rosemann, Mining configurable enterprise information systems, Data and Knowledge Engineering, vol.56, no.3, pp.195-244, 2006.
7. A. K. A. de Medeiros, W. M. P. van der Aalst, and A. J. M. M. Weijters, Quantifying process equivalence based on observed behavior, Data and Knowledge Engineering, vol.64, no.1, pp.55-74, 2008.
8. H. Kim, J.-Y. Jung, and S.-H. Kang, Extensible collaborative process composition using workflow inheritance, IE Interfaces, vol.16, pp.49-54, 2003.
9. J. Bae, B. Kwon, J.-Y. Jung, and S.-H. Kang, Workflow collaboration design using similarity measures among process definitions, Review of Korean Society for Internet Info., vol.6, no.1, pp.52-61, 2005.
10. J. Jung and J. Bae, Workflow clustering method based on business process similarity, in Computer Science and Applications, M. Gavrilova et al. (eds.), Berlin Heidelberg, Springer-Verlag, LNCS 3981, pp.379-389, 2006.
11. J. Cardoso, How to Measure the control-flow complexity of web processes and workflows, in Workflow Handbook 2005, L. Fischer (eds.), WfMC, Lighthouse Point, FL, pp.199-212, 2005.
12. H. A. Reijers and I. T. P. Vanderfeesten, Cohesion and coupling metrics for workflow process design, in Business Process Management, J. Desel, B. Pernici, and M. Weske (eds.), Berlin Heidelberg, Springer-Verlag, LNCS 3080, pp.290-305, 2004.
13. L. Zhao, L. Qu, and M. Liu, Disruption coordination of closed-loop supply chain network (I) - Analysis and simulations, International Journal of Innovative Computing, Information and Control, vol.4, no.11, pp.2955-2964, 2008.
14. OMG, Business Process Modeling Notation (BPMN) Specification, Final Adopted Specification, dtc/06-02-101, Object Management Group, 2006.
15. J. Mendling and W. M. P. van der Aalst, Formalization and verification of EPCs with OR-joins based on state and context, Proc. of the 19th Int. Conf. on Advanced Info. Systems Eng., pp.439-453, 2007.
16. W. M. P. van der Aalst and A. ter Hofstede, YAWL: Yet another workflow language, Information Systems, vol.30, pp.245-275, 2005.
17. Workflow Management Coalition, Workflow Process Definition Interface - XML Process Definition Language Version 2.0, WFMC-TC-1025, WfMC, 2005.
18. R. Agarwal and A. P. Sinha, Object-oriented modeling with UML: A study of developers' perceptions, Communications of ACM, vol.46, no.9, pp.248-256, 2003.
19. Y. Kusunoki, M. Inuiguchi, and J. Stefanowski, Rule induction via clustering decision classes, International Journal of Innovative Computing, Information and Control, vol.4, no.10, pp.2663-2677, 2008.