Mining expressive process models by clusteringworkflow traces

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3056, Issue , 2004, Pages 52-62

  Greco, Gianluigi ^a   Guzzo, Antonella ^a   Pontieri, Luigi ^b   Saccà, Domenico ^a,b  

^a UNIVERSITY OF CALABRIA   (Italy)

^b ICAR CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

SPECIFICATION LANGUAGES; COMPUTER PROGRAMMING; CONSTRAINT THEORY; GRAPHIC METHODS; MATHEMATICAL MODELS;

GLOBAL CONSTRAINTS; LOCAL CONSTRAINTS; LOCAL PROPERTY; ORIGINAL MODEL; PROCESS MINING; PROCESS MODEL; WORKFLOW EXECUTION;

DATA MINING; ARTIFICIAL INTELLIGENCE;

CONSTRAINT SOLVING; DATA SETS; SHANNON INFORMATION; SUBSPACE CLUSTERING;

EID: 7444250376     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24775-3_8     Document Type: Conference Paper

References (18)

1. R. Agrawal, D. Gunopulos, and F. Leymann. Mining process models from workflow logs. In Proc. 6th Int. Conf. on Extending Database Technology (EDBT’98), pages 469-483, 1998.
2. R. Agrawal, T. Imielinski, and A. Swami. Mining Association Rules between Sets of Items in Large Databases. In Proc. of SIGMOD’93, pages 207-216, 1993.
3. R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. In Proc. of the 20th Int’l Conference on Very Large Databases, pages 487-499, 1994.
4. J.E. Cook and A.L. Wolf. Automating process discovery through event-data analysis. In Proc. 17th Int. Conf. on Software Engineering (ICSE’95), pages 73-82, 1995.
5. H. Davulcu, M. Kifer, C.R. Ramakrishnan, and I.V. Ramakrishnan. Logic based modeling and analysis of workflows. In Proc. of PODS’98, pages 25-33, 1998.
6. G. Greco, A. Guzzo, G. Manco, and D. Saccà. Mining Frequent Instances on Workflows. In Proc. of PAKDD’03, pages 209-221, 2003.
7. J. Han, J. Pei, andY. Yi. Mining frequent patterns without candidate generation. In Proc. Int. ACM Conf. on Management of Data (SIGMOD’00), pages 1-12, 2000.
8. Y. Kim, W. Nick Street, and F. Menczer. Feature selection in unsupervised learning via evolutionary search. In Proceedings of the KDD’00, pages 365-369, 2000.
9. V. I. Levenshtein. Binary codes capable of correcting deletions, insertions and reversals. Doklady Akademii Nauk SSSR 163(4) page 845-848, 1965.
10. N. Lesh, M.J. Zaki, and M. Ogihara. Mining features for sequence classification. In Proc. of KDD’99, pages 342-346, 1999.
11. H. Motoda and H. Liu. Data reduction: feature selection. pages 208-213, 2002.
12. P. Muth, J. Weifenfels, M.Gillmann, and G. Weikum. Integrating light-weight workflow management systems within existing business environments. In Proc. 15th IEEE Int. Conf. on Data Engineering (ICDE’99), pages 286-293, 1999.
13. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M. Hsu. Prefixspan: Mining sequential patterns by prefix-projected growth. In Proc. of ICDE’2001, pages 215-224, 2001.
14. B. Padmanabhan and A. Tuzhilin. Small is beautiful: discovering the minimal set of unexpected patterns. In Proc. of the 6th ACM SIGKDD, pages 54-63, 2000.
15. W.M.P. van der Aalst. The application of petri nets to worflow management. Journal of Circuits, Systems, and Computers, 8(1):21-66, 1998.
16. W.M.P. van der Aalst, A. Hirnschall, and H.M.W. Verbeek. An alternative way to analyze workflow graphs. In Proc. 14th Int. Conf. on Advanced Information Systems Engineering, pages 534-552, 2002.
17. W.M.P. van der Aalst and K.M. van Hee. Workflow Management: Models, Methods, and Systems. MIT Press, 2002.
18. W.M.P. van der Aalst, B.F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A.J.M.M. Weijters. Workflow mining: A survey of issues and approaches. Data and Knowledge Engineering, 47(2):237-267,2003