Algorithms for anomaly detection of traces in logs of process aware information systems

Information Systems

Volumn 38, Issue 1, 2013, Pages 33-44

  Bezerra, Fábio ^a   Wainer, Jacques ^b  

^a UFRA   (Brazil)

^b UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Anomaly detection; Process mining; Process aware systems

Indexed keywords

HARDWARE; INFORMATION SYSTEMS;

ANOMALY DETECTION; EFFECTIVE SOLUTION; PROCESS MINING; PROCESS MODELING; PROCESS-AWARE INFORMATION SYSTEMS; SAMPLING ALGORITHM;

ITERATIVE METHODS;

EID: 84869236182     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.is.2012.04.004     Document Type: Article

References (49)

1. R. Accorsi, C. Wonnemann, Auditing workflow executions against dataflow policies, in: Business Information Systems, Lecture Notes in Business Information Processing, vol. 47, Springer, 2010, pp. 207-217.
2. R. Accorsi, C. Wonnemann, T. Stocker, Towards forensic data flow analysis of business process logs, in: Sixth International Conference on IT Security Incident Management and IT Forensics (IMF), IEEE Press, 2011, pp. 3-20.
3. R. Agrawal, D. Gunopulos, F. Leymann, Mining process models from workflow logs, in: EDBT '98: Proceedings of the 6th International Conference on Extending Database Technology, Lecture Notes in Computer Science, vol. 1377, Springer, 1998, pp. 469-483. (Pubitemid 128044768)
4. F. Bezerra, J. Wainer, Anomaly detection algorithms in business process logs, in: 10th International Conference on Enterprise Information Systems, SciTePress, 2008, pp. 11-18.
5. F. Bezerra, J. Wainer, Anomaly detection algorithms in logs of process aware systems, in: SAC'08: Proceedings of the ACM Symposium on Applied Computing, 2008, pp. 951-952.
6. F. Bezerra, J. Wainer, Auditing workflow logs for fraud detection, in: Proceedings of the KDD 2008 Workshop on Data Mining for Business Applications, 2008, pp. 1-5, /http://labs.accenture.com/kdd2008-workshop/dmba-proceedings. pdfS, accessed 12/2011.
7. F. Bezerra, J. Wainer, Fraud detection in process aware systems, International Journal of Business Process Integration and Management (IJBPIM) 5 (2) (2011) 121-129.
8. F. Bezerra, J. Wainer, W.M.P. Aalst, Anomaly detection using process mining, in: Enterprise, Business-Process and Information Systems Modeling, Lecture Notes in Business Information Processing, vol. 29, Springer, 2009, pp. 149-161.
9. R.P.J.C. Bose, W.M.P. van der Aalst, Process diagnostics using trace alignment: opportunities, issues, and challenges, Information Systems 37 (2012) 117-141.
10. V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: a survey, ACM Computing Surveys 41 (3) (2009) 1-58.
11. J.E. Cook, Z. Du, C. Liu, A.L. Wolf, Discovering models of behavior for concurrent workflows, Computers in Industry 53 (3) (2004) 297-319.
12. J.E. Cook, A.L. Wolf, Discovering models of software processes from event-based data, ACM Transactions on Software Engineering and Methodology 7 (3) (1998) 215-249. (Pubitemid 128658170)
13. A. de Medeiros, W. van der Aalst, A. Weijters, Workflow mining: current status and future directions, in: On The Move to Meaningful Internet Systems: CoopIS, DOA, and ODBASE, Lecture Notes in Computer Science, vol. 2888, Springer, 2003, pp. 389-406.
14. A.K.A. de Medeiros, Genetic Process Mining. Ph.D. Thesis, Tech-nische Universiteit Eindhoven, Eindhoven, The Netherlands, 2006.
15. A.K.A. de Medeiros, A.J.M.M. Weijters, W.M.P. van der Aalst, Genetic process mining: a basic approach and its challenges, in: Business Process Management Workshops, Lecture Notes in Computer Science, vol. 3812, Springer, September 2006, pp. 203-215.
16. S. Donoho, Early detection of insider trading in option markets, in: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD'04, ACM Press, pp. 420-429.
17. M. Dumas, W. van der Aalst, A. ter Hofstede, Process-Aware Information Systems: Bridging People and Software through Process Technology, Wiley, 2005 ISBN 13 978-0-471-66306-5.
18. C. Ellis, K. Kim, A. Rembert, J. Wainer, Investigations on stochastic information control nets, Information Sciences, in press. Available in /http://dx.doi.org/10.1016/j.ins.2011.07.031S.
19. T. Fawcett, F. Provost, Adaptive fraud detection, Data Mining and Knowledge Discovery 1 (January) (1997) 291-316.
20. S. Goedertier, D. Martens, J. Vanthienen, B. Baesens, Robust process discovery with artificial negative events, Journal of Machine Learning Research 10 (2009) 1305-1340.
21. G. Greco, A. Guzzo, L. Ponieri, D. Sacca, Discovering expressive process models by clustering log traces, IEEE Transactions on Knowledge and Data Engineering 18 (8) (2006) 1010-1027. (Pubitemid 44109532)
22. M. Hammori, J. Herbst, N. Kleiner, Interactive workflow mining-requirements, concepts and implementation, Data & Knowledge Engineering 56 (1) (2006) 41-63. (Pubitemid 41542967)
23. J. Herbst, D. Karagiannis, Workflow mining with InWoLvE, Computers in Industry 53 (3) (2004) 245-264.
24. W. Lee, D. Xiang, Information-theoretic measures for anomaly detection, in: IEEE Symposium on Security and Privacy, 2001, pp. 130-143. (Pubitemid 32882632)
25. D. Neiger, L. Churilov, A. Flitman, D. Neiger, L. Churilov, A. Flitman, Introducing value-focused process engineering, in: Value-Focused Business Process Engineering: A Systems Approach, Integrated Series in Information Systems, vol. 19, Springer, 2009, pp. 1-13.
26. C.C. Noble, D.J. Cook, Graph-based anomaly detection, in: KDD'03: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, 2003, pp. 631-636.
27. S. Pandit, D.H. Chau, S. Wang, C. Faloutsos, Netprobe: a fast and scalable system for fraud detection in online auction networks, in: WWW'07: Proceedings of the 16th International Conference on World Wide Web, ACM Press, 2007, pp. 201-210. (Pubitemid 47582250)
28. S.S. Pinter, M. Golani, Discovering workflow models from activities' lifespans, Computers in Industry 53 (3) (2004) 283-296.
29. A. Rozinat, A.A. de Medeiros, C. Günther, A. Weijters, W. van der Aalst, Towards an Evaluating Framework for Process Mining Algorithms, Technical Report, Technische Universiteit Eindhoven, 2007.
30. A. Rozinat, W. van der Aalst, Conformance checking of processes based on monitoring real behavior, Information Systems 33 (March (1)) (2008) 64-95. (Pubitemid 350051578)
31. A. Rozinat, W.M.P. van der Aalst, Conformance testing: measuring the fit and appropriateness of event logs and process models, in: Business Process Management Workshops, Lecture Notes in Computer Science, vol. 3812, Springer, 2005, pp. 163-176. (Pubitemid 43899920)
32. G. Schimm, Mining exact models of concurrent workflows, Computers in Industry 53 (3) (2004) 265-281.
33. B. Shishkov, M. Sinderen, A. Verbraeck, Towards flexible inter-enterprise collaboration: a supply chain perspective, in: Enterprise Information Systems, Lecture Notes in Business Information Processing, vol. 24, Springer, pp. 513-527.
34. W. Steinberg, Statistics Alive!, second ed., SAGE Publications, 2010, pp. 318-324 (Chapter 26 - Tukey HSD Test).
35. W.M. van Aalst, K.M. van Hee, J.M. van Werf, M. Verdonk, Auditing 2.0: using process mining to support tomorrow's auditor, Computer 43 (March (3)) (2010) 90-93.
36. W.M.P. van der Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer, 2011.
37. W.M.P. van der Aalst, A.K.A. de Medeiros, Process mining and security: detecting anomalous process executions and checking process conformance, Electronic Notes in Theoretical Computer Science 121 (February (4)) (2005) 3-21. (Pubitemid 40219700)
38. W.M.P. van der Aalst, B.F. van Dongen, J. Herbst, L. Maruster, G. Schimm, A.J.M.M. Weijters, Workflow mining: a survey of issues and approaches, Data & Knowledge Engineering 47 (2) (2003) 237-267.
39. W.M.P. van der Aalst, A.J.M.M. Weijters, Process mining: a research agenda, Computers in Industry 53 (3) (2004) 231-244.
40. W.M.P. van der Aalst, T. Weijters, L. Maruster, Workflow mining: discovering process models from event logs, IEEE Transactions on Knowledge and Data Engineering 16 (9) (2004) 1128-1142.
41. B. van Dongen, A. de Medeiros, H. Verbeek, A. Weijters, W. van der Aalst, The ProM framework: a new era in process mining tool support, in: Applications and Theory of Petri Nets, Lecture Notes in Computer Science, vol. 3536, Springer, 2005, pp. 444-454. (Pubitemid 41422025)
42. C.J. van Rijsbergen, Information Retrieval, Butterworths, 1979.
43. I. Vanderfeesten, H. Reijers, J. Mendling, W. van der Aalst, J. Cardoso, On a quest for good process models: the cross-connectivity metric, in: Advanced Information Systems Engineering, Lecture Notes in Computer Science, vol. 5074, Springer, 2008, pp. 480-494.
44. J. Wainer, K. Kim, C.A. Ellis, A workflow mining method through model rewriting, in: Groupware: Design, Implementation, and Use: 11th International Workshop, Lecture Notes in Computer Science, vol. 3706, Springer, September 2005, pp. 184-191.
45. M. Weidlich, A. Polyvyanyy, N. Desai, J. Mendling, M. Weske, Process compliance analysis based on behavioural profiles, Information Systems, in press. Available in /http://dx.doi.org/10.1016/j. is.2011.04.002S.
46. A. Weijters, W. van der Aalst, Rediscovering workflow models from event-based data using little thumb, Integrated Computer-Aided Engineering 10 (2) (2003) 151-162.
47. A. Weijters, W. van der Aalst, A.A. de Medeiros, Process Mining with the Heuristics Miner Algorithm, Technical Report, Beta Research School for Operations Management and Logistics, 2006.
48. L. Wen, J. Wang, J. Sun, Detecting implicit dependencies between tasks from event logs, in: Frontiers of WWW Research and Development - APWeb 2006, Lecture Notes in Computer Science, vol. 3841, Springer, 2006, pp. 591-603. (Pubitemid 43973222)
49. W.-S. Yang, S.-Y. Hwang, A process-mining framework for the detection of healthcare fraud and abuse, Expert Systems with Applications 31 (July (1)) (2006) 56-68.