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Smart Information and Knowledge Management pp 103–127Cite as

Incremental Declarative Process Mining

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 260))

Abstract

Business organizations achieve their mission by performing a number of processes. These span from simple sequences of actions to complex structured sets of activities with complex interrelation among them. The field of Business Processes Management studies how to describe, analyze, preserve and improve processes. In particular the subfield of Process Mining aims at inferring a model of the processes from logs (i.e. the collected records of performed activities). Moreover, processes can change over time to reflect mutated conditions, therefore it is often necessary to update the model. We call this activity Incremental Process Mining. To solve this problem, we modify the process mining system DPML to obtain IPM (Incremental Process Miner), which employs a subset of the \(\mathcal{S}\)CIFF language to represent models and adopts techniques developed in Inductive Logic Programming to perform theory revision. The experimental results show that is more convenient to revise a theory rather than learning a new one from scratch.

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References

  1. Dumas, M., Reichert, M., Shan, M.-C. (eds.): BPM 2008. LNCS, vol. 5240. Springer, Heidelberg (2008)

    Google Scholar 

  2. van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: A survey of issues and approaches. Data Knowl. Eng. 47(2), 237–267 (2003)

    Article  Google Scholar 

  3. Alberti, M., Chesani, F., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: Verifiable agent interaction in abductive logic programming: The sciff framework. ACM Trans. Comput. Log. 9(4) (2008)

    Google Scholar 

  4. Alberti, M., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: An abductive interpretation for open societies. In: Cappelli, A., Turini, F. (eds.) AI*IA 2003. LNCS (LNAI), vol. 2829. Springer, Heidelberg (2003)

    Google Scholar 

  5. Lamma, E., Mello, P., Riguzzi, F., Storari, S.: Applying inductive logic programming to process mining. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 132–146. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  6. Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19 20, 629–679 (1994)

    Article  Google Scholar 

  7. Raedt, L.D., Dzeroski, S.: First-order jk-clausal theories are pac-learnable. Artif. Intell. 70(1-2), 375–392 (1994)

    Article  MATH  Google Scholar 

  8. De Raedt, L., Van Laer, W.: Inductive constraint logic. In: Zeugmann, T., Shinohara, T., Jantke, K.P. (eds.) ALT 1995. LNCS (LNAI), vol. 997, Springer, Heidelberg (1995)

    Google Scholar 

  9. Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy theory revision: Induction and abduction in inthelex. Machine Learning 38(1-2), 133–156 (2000)

    Article  MATH  Google Scholar 

  10. Clark, K.L.: Negation as failure. In: Logic and Databases. Plenum Press, New York (1978)

    Google Scholar 

  11. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of the Fifth International Conference and Symposium on Logic Programming, pp. 1070–1080 (1988)

    Google Scholar 

  12. Van Gelder, A., Ross, K.A., Schlipf, J.S.: The well-founded semantics for general logic programs. Journal of the ACM 38(3), 620–650 (1991)

    MATH  Google Scholar 

  13. De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26(2-3), 99–146 (1997)

    Article  MATH  Google Scholar 

  14. Van Laer, W.: ICL manual, http://www.cs.kuleuven.be/~ml/ACE/DocACEuser.pdf

  15. Adé, H., Malfait, B., Raedt, L.D.: Ruth: an ilp theory revision system. In: Ras, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 336–345. Springer, Heidelberg (1994)

    Google Scholar 

  16. Richards, B.L., Mooney, R.J.: Automated refinement of first-order horn-clause domain theories. Machine Learning 19(2), 95–131 (1995)

    Google Scholar 

  17. Georgakopoulos, D., Hornick, M.F., Sheth, A.P.: An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and Parallel Databases 3(2), 119–153 (1995)

    Article  Google Scholar 

  18. Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  19. Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18(8), 1010–1027 (2006)

    Article  Google Scholar 

  20. Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  21. Montali, M., Pesic, M., van der Aalst, W.M.P., Chesani, F., Mello, P., Storari, S.: Declarative specification and verification of service choreographies. ACM Transactions on The Web (2009)

    Google Scholar 

  22. van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)

    Article  Google Scholar 

  23. van Dongen, B.F., van der Aalst, W.M.P.: Multi-phase process mining: Building instance graphs. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, T.-W. (eds.) ER 2004. LNCS, vol. 3288, pp. 362–376. Springer, Heidelberg (2004)

    Google Scholar 

  24. Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Inducing declarative logic-based models from labeled traces. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 344–359. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  25. Chesani, F., Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Exploiting inductive logic programming techniques for declarative process mining. In: ToPNoC II 2009. LNCS, vol. 5460, pp. 278–295. Springer, Heidelberg (2009)

    Google Scholar 

  26. Apt, K.R., Bezem, M.: Acyclic programs. New Generation Comput. 9(3/4), 335–364 (1991)

    Article  Google Scholar 

  27. Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: Full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), pp. 287–300. IEEE Computer Society Press, Los Alamitos (2007)

    Chapter  Google Scholar 

  28. Chavez, A., Maes, P.: Kasbah: An agent marketplace for buying and selling goods. In: Proceedings of the First International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology (PAAM 1996), London, April 1996, pp. 75–90 (1996)

    Google Scholar 

  29. Chesani, F.: Socs protocol repository, http://edu59.deis.unibo.it:8079/SOCSProtocolsRepository/jsp/index.jsp

  30. Desel, J., Erwin, T.: Hybrid specifications: looking at workflows from a run-time perspective. Int. J. Computer System Science & Engineering 15(5), 291–302 (2000)

    Google Scholar 

  31. Ferreira, H.M., Ferreira, D.R.: An integrated life cycle for workflow management based on learning and planning. Int. J. Cooperative Inf. Syst. 15(4), 485–505 (2006)

    Article  Google Scholar 

  32. Goedertier, S.: Declarative techniques for modeling and mining business processes. PhD thesis, Katholieke Universiteit Leuven, Faculteit Economie en Bedrijfswetenschappen (2008)

    Google Scholar 

  33. Wrobel, S.: First order theory refinement. In: Raedt, L.D. (ed.) Advances in Inductive Logic Programming, pp. 14–33. IOS Press, Amsterdam (1996)

    Google Scholar 

  34. Reichert, M., Rinderle-Ma, S., Dadam, P.: Flexibility in process-aware information systems. T. Petri Nets and Other Models of Concurrency 2, 115–135 (2009)

    Article  Google Scholar 

  35. Mutschler, B., Reichert, M., Rinderle, S.: Analyzing the dynamic cost factors of process-aware information systems: A model-based approach. In: Krogstie, J., Opdahl, A.L., Sindre, G. (eds.) CAiSE 2007 and WES 2007. LNCS, vol. 4495, pp. 589–603. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. ENDIF, Università di Ferrara, Via Saragat, 1, 44100, Ferrara, Italy

    Massimiliano Cattafi, Evelina Lamma, Fabrizio Riguzzi & Sergio Storari

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  1. Massimiliano Cattafi
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  2. Evelina Lamma
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  3. Fabrizio Riguzzi
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  4. Sergio Storari
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Editors and Affiliations

  1. Faculty of Management and Economics, Gdansk University of Technology, Str.Narutowicza 11/12, 80-233, Gdansk, Poland

    Edward Szczerbicki

  2. Institute of Informatics, Wroclaw University of Technology, Str.Wyb.Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Cattafi, M., Lamma, E., Riguzzi, F., Storari, S. (2010). Incremental Declarative Process Mining. In: Szczerbicki, E., Nguyen, N.T. (eds) Smart Information and Knowledge Management. Studies in Computational Intelligence, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04584-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-04584-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04583-7

  • Online ISBN: 978-3-642-04584-4

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