Temporal mining for interactive workflow data analysis

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2009, Pages 109-117

  Berlingerio, Michele ^a   Pinelli, Fabio ^a   Nanni, Mirco ^a   Giannotti, Fosca ^a  

^a ISTI CNR   (Italy)

Author keywords

Temporal sequence mining; Workflow mining

Indexed keywords

ANALYSIS PROCESS; CERTAIN HYPOTHESIS; CONDENSED REPRESENTATIONS; DATA ANALYSIS; DOMAIN EXPERTS; IDENTICAL PROCESS; IN-PROCESS; INPUT DATAS; INTERACTIVE EXPLORATION; INTERACTIVE WORKFLOW; RE-AGGREGATION; SEQUENTIAL ORDERING; SEQUENTIAL PATTERNS; TEMPORAL INFORMATION; TEMPORAL SEQUENCE MINING; TIME STAMPS; TRANSITION TIME; WORKFLOW MINING; WORKFLOW MODELS;

MANAGEMENT; MINING;

DATA MINING;

EID: 70350653778     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557038     Document Type: Conference Paper

References (26)

1. The think3 company. http://www.think3.com.
2. Rakesh Agrawal, Dimitrios Gunopulos, and Frank Leymann. Mining process models from workflow logs. volume 1377-469+ of LNCS, '98.
3. Michele Berlingerio, Francesco Bonchi, Fosca Giannotti, and Franco Turini. Mining clinical data with a temporal dimension: a case study. In Proc. of The 1st Intern.Conf. on Bioinf. and Biomed., '07.
4. Michele Berlingerio, Francesco Bonchi, Fosca Giannotti, and Franco Turini. Time-annotated sequences for medical data mining. In Proc. of The Intern. Workshop of Data Min. in Medicine, 2007.
5. A. Datta. Automating the discovery of AI-IS business process models: probabilistic and algorithmic approaches. Inf. Sys. Res., 9(3):275-301, '98.
6. P. Lawrence (ed). Workflow Handbook 1997, Workflow Management Coalition. J. Wiley and S., NY, 1997.
7. Fosca Giannotti, Mirco Nanni, and Dino Pedreschi. Efficient mining of temporally annotated sequences. In Proc. of the 6th SIAM Intern. Conf. on Data Min., 2006.
8. Fosca Giannotti, Mirco Nanni, Dino Pedreschi, and Fabio Pinelli. Trajectory patter mining. In The 30th KDD Int.Conf. on Knowl.Disc. and Data Min., '07.
9. Fosca Giannotti, Mirco Nanni, Dino Pedreschi, and Fabio Pinelli. Mining sequences with temporal annotations. In Proc. of the 2006 ACM Symp. on Applied Comp. (SAC), pages 593-597, 2006.
10. Gianluigi Greco, Antonella Guzzo, Giuseppe Manco, and Domenico Saccí. Mining unconnected patterns in workflows. Inf. Syst., 32(5):685-712, 2007.
11. Gianluigi Greco, Antonella Guzzo, Luigi Pontieri, and Domenico Saccá. Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng., 18(8):1010-1027, 2006.
12. San-Yih Hwang, Chih-Ping Wei, and Wan-Shiou Yang. Discovery of temporal patterns from process instances. Comput. Ind., 53(3):345-364, 2004.
13. San-Yih Hwang and Wan-Shiou Yang. On the discovery of process models from their instances. Decis. Support Syst., 34(1):41-57, 2002.
14. Steffen Kempe and Jochen Hipp. Mining sequences of temporal intervals. In PKDD, pages 569-576, 2006.
15. Frank Klawonn. Finding informative rules in interval sequences. In Intelligent Data Analysis, pages 123-132. Springer, 2001.
16. Hongyan Ma. Process-aware information systems: Bridging people and software through process technology: Book reviews. J. Am. Soc. Inf. Sci. Technol., 58(3):455-456, 2007.
17. Fabian Moerchen. Algorithms for time series knowledge mining. In Proc. of the 12th SIGKDD int.conf. on Knowl.disc. and data min., 2006.
18. Panagiotis Papapetrou, George Kollios, Stan Sclaroff, and Dimitrios Gunopulos. Discovering frequent arrangements of temporal intervals. In ICDM, 2005.
19. Dhaval Patel, Wynne Hsu, and Mong Li Lee. Mining relationships among interval-based events for classification. In Proc. of the 2008 int.conf. on Manag.of data, pages 393-404, 2008.
20. Po shan Kam and Ada Wai chee Fu. Discovering temporal patterns for interval-based events. In Proc. of the 2nd DaWaK, pages 317-326. Springer, 2000.
21. S.Jablonski and C.Bussler. Workflow Management: Modeling Concepts, Architecture and Implementation. Intern. Thomson Comp. Press, 1996.
22. 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 Knowl. Eng., 47(2):237-267, 2003.
23. Wil M. P. van der Aalst, Jörg Desel, and Andreas Oberweis, editors. Business Process Management, Models, Techniques, and Empirical Studies, volume 1806 of LNCS. Springer, 2000.
24. Wil M. P. van der Aalst and Kees M. van Hee. Workflow Management: Models, Methods, and Systems. MIT Press, 2002.
25. T. Weijters and W. M. P. van der Aalst. Process mining: Discovering workflow models from event-based data., 2001.
26. Edi Winarko and John F. Roddick. Discovering richer temporal association rules from interval-based data. In A. Min Tjoa and J. Trujillo, editors, 7th DaWaK, volume 3589 of LNCS, pages 315-325. Springer, '05.