Using classification methods to label tasks in process mining

Journal of Software Maintenance and Evolution

Volumn 22, Issue 6-7, 2010, Pages 497-517

  Buffett, Scott ^a   Geng, Liqiang ^a  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

Bayesian classification; Process mining; Task labeling; Workflow

Indexed keywords

UNCERTAINTY ANALYSIS;

BAYESIAN CLASSIFICATION; BUSINESS PROCESS; CLASSIFICATION METHODS; CLASSIFICATION SCHEME; CLUSTERING ENGINES; INDEPENDENT TASKS; PROCESS MINING; WORKFLOW;

DATA MINING;

EID: 78649564381     PISSN: 1532060X     EISSN: 15320618     Source Type: Journal    
DOI: 10.1002/smr.463     Document Type: Article

References (26)

1. van der Aalst WMP, Weijters AJMM, Maruster L,. Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 2004; 16 (9): 1128-1142.
2. Agrawal R, Gunopulos D, Leymann F,. Mining process models from workflow logs. Proceedings of the Sixth International Conference on Extending Database Technology, Valencia, Spain, 1998; 469-483.
3. Verbeek HMW, Pretorius AJ, van der Aalst WMP, van Wijk JJ,. On petri-net synthesis and attribute-based visualization. Proceedings of the Workshop on Petri Nets and Software Engineering (PNSE'07), Siedlce, Poland, 2007; 127-142.
4. Cook JE, Wolf AL,. Software process validation: Quantitatively measuring the correspondence of a process to a model. ACM Transactions on Software Engineering and Methodology 1999; 8 (2): 147-176.
5. Rozinat A, van der Aalst WMP,. Conformance testing: Measuring the fit and appropriateness of event logs and process models. Proceedings of the First International Workshop on Business Process Intelligence (BPI'05), Nancy, France, 2005; 1-12.
6. Kushmerick N, Lau TA,. Automated email activity management: An unsupervised learning approach. Proceedings of the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, 2005; 67-74.
7. Buffett S, Geng L,. Bayesian classification of events for task labeling using workflow models. Proceedings of the Fourth Workshop on Business Process Intelligence (BPI 08), Milan, Italy, 2008.
8. van der Aalst WMP,. Process mining and monitoring processes and services: Workshop report. The Role of Business Processes in Service Oriented Architectures, Number 6291 in Dagstuhl Seminar Proceedings, Dagstuhl, Germany, Leymann F, Reisig W, Thatte SR, van der Aalst WMP (eds.), 2006.
9. van der Aalst WMP, van Dongen BF, Herbst J, Maruster L, Schimm G, Weijters AJMM,. Workflow mining: A survey of issues and approaches. Data and Knowledge Engineering 2003; 47 (2): 237-267.
10. Huang Y, Govindaraju D, Mitchell TM, de Carvalho VR, Cohen WW,. Inferring ongoing activities of workstation users by clustering email. Proceedings of the First Conference on Email and Anti-Spam, Mountain View CA, U.S.A., 2004.
11. Li H, Shen D, Zhang B, Chen A, Yang Q,. Adding semantics to email clustering. Proceedings Sixth IEEE International Conference on Data Mining, Hong Kong, China, 2006; 938-942.
12. Dredze M, Lau TA, Kushmerick N,. Automatically classifying emails into activities. Proceedings of the 2006 International Conference on Intelligent user Interfaces, Sydney, Australia, 2006; 70-77.
13. Dredze M, Wallach HM, Puller D, Pereira F,. Generating summary keywords for emails using topics. Proceedings of the 2008 International Conference on Intelligent user Interfaces, Canary Islands, Spain, 2008; 199-206.
14. Geng L, Korba L, Wang Y, Wang X, You Y,. Finding topics in email using formal concept analysis and fuzzy membership functions. Proceedings of the Canadian Conference on AI, Windsor, ON, Canada, 2008; 108-113.
15. Khoussainov R, Kushmerick N,. Email task management: An iterative relational learning approach. Second Conference on Email and Anti-Spam (CEAS), Stanford University, California, U.S.A., 2005.
16. Silva R, Zhang J, Shanahan JG,. Probabilistic workflow mining. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, U.S.A., 2005; 275-284.
17. Greco G, Guzzo A, Manco G, Saccà D,. Mining frequent instances on workflows. Proceedings of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Seoul, Korea, 2003; 209-221.
18. Peterson JL,. Petri nets. ACM Computing Surveys 1977; 9 (3): 223-252.
19. Dustdar S, Hoffmann T, van der Aalst WMP,. Mining of ad-hoc business processes with TeamLog. Data and Knowledge Engineering 2005; 55 (2): 129-158.
20. Greco G, Guzzo A, Pontieri L,. Mining taxonomies of process models. Data and Knowledge Engineering 2008; 67 (1): 74-102.
21. Ghionna L, Greco G, Guzzo A, Pontieri L,. Outlier detection techniques for process mining applications. ISMIS 2008, Toronto, Canada, 2008; 150-159.
22. Huang J, Lu J, Ling XC,. Comparing naive Bayes, decision trees, and SVM with AUC and accuracy. Proceedings Third IEEE International Conference on Data Mining (ICDM 2003), Melbourne, U.S.A., 2003; 553-556.
23. Zhang H,. Exploring conditions for the optimality of naîve Bayes. International Journal of Pattern Recognition and Artificial Intelligence 2005; 19 (2): 183-198.
24. Dai W, Xue G, Yang Q, Yu Y,. Transferring naive Bayes classifiers for text classification. Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence (AAAI2007), Vancouver, Canada, 2007; 540-545.
25. He F, Ding X,. Improving naive Bayes text classifier using smoothing methods. Proceedings of the 29th European Conference on IR Research (ECIR), Rome, Italy, 2007; 703-707.
26. Kim S, Han K, Rim H, Myaeng S,. Some effective techniques for naive bayes text classification. IEEE Transactions on Knowledge and Data Engineering 2006; 18 (11): 1457-1466.