### Anomaly detection in several running processes

#### Abstract

The paper discusses the problem of process mining with further anomalies detection for constructed process models. The algorithms extension for the case of several running processes with some restrictions on their structure has been investigated. An acyclic directed graph is considered as a formal model for a process. The event log traces containing the data from one and several processes were examined. The complexity problem of detecting anomalies is studied, for the cases when the processes consist of various actions sets and when the intersection of the actions sets of the processes is a finite set. The applicability limitations of the proposed algorithms’ extension are found. The complexity estimates for the formal models’ construction of a set of processes problem and for anomaly detection with constructed formal models’ problem are determined. For the case when data from several processes are encountered within the same trace some additional estimates are given, concerning the additional required memory and the minimum size of the log.

#### Full Text:

PDF (Russian)#### References

R. Sarno, F. Sinaga, K. R. Sungkono, Anomaly detection in business processes using process mining and fuzzy association rule learning. Journal of Big Data, 2020, vol. 7, no. 1, pp. 1-19.

J. Cremerius, M. Weske, Data-Enhanced Process Models in Process Mining. arXiv preprint arXiv:2107.00565, 2021.

R. Bhogal, A. Garg, Anomaly Detection and Fault Prediction of Breakdown to Repair Process Using Mining Techniques. International Conference on Intelligent Engineering and Management (ICIEM), IEEE, 2020, pp. 240-245.

K. Diba et al., Extraction, correlation, and abstraction of event data for process mining. Wiley Interdisciplinary Reviews: Data Mining

and Knowledge Discovery, 2020, vol. 10, no. 3, p. e1346.

A. Pika et al., Privacy-preserving process mining in healthcare. International journal of environmental research and public health, 2020, vol. 17, no. 5, p. 1612.

E. M. Gold, Complexity of automaton identification from given data. Information and control, 1978, vol. 37, no. 3, pp. 302-320.

L. Miclet, Grammatical inference. Syntactic and Structural Pattern Recognition—Theory and Applications, 1990, pp. 237-290.

W. Van der Aalst, T. Weijters, L. Maruster, Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 2004, vol. 16, no. 9, pp. 1128-1142.

W. Van Der Aalst, K. M. Van Hee, K. van Hee, Workflow management: models, methods, and systems. MIT press, 2004.

J. E. Cook, A. L. Wolf, Discovering models of software processes from event-based data. ACM Transactions on Software Engineering and Methodology (TOSEM), 1998, vol. 7, no. 3, pp. 215-249.

S. Das, M. C. Mozer, A unified gradient-descent/clustering architecture for finite state machine induction. Advances in neural information processing systems, 1994, pp. 19-26.

Z. Zeng, R. M. Goodman, P. Smyth, Learning finite state machines with self-clustering recurrent networks. Neural Computation, 1993, vol. 5, no. 6, pp. 976-990.

R. Agrawal, R. Srikant, Fast algorithms for mining association rules. Proc. 20th int. conf. very large data bases, VLDB, 1994, vol. 1215, pp. 487-499.

R. Agrawal, R. Srikant, Mining sequential patterns. Proceedings of the eleventh international conference on data engineering, IEEE, 1995, pp. 3-14.

R. Agrawal, D. Gunopulos, F. Leymann, Mining process models from workflow logs. International Conference on Extending Database Technology, Springer, Berlin, Heidelberg, 1998, pp. 467-483.

H. Mannila, H. Toivonen, A. I. Verkamo, Discovery of frequent episodes in event sequences. Data mining and knowledge discovery, 1997, vol. 1, no. 3, pp. 259-289.

R. Agrawal, T. Imieliński, A. Swami, Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 1993, pp. 207-216.

I. Yu. Teryokhina, A. A. Grusho, E. E. Timonina, S. Ya. Shorgin, Constructing process models represented by simple Petri nets. Systems and Means of Informatics, 2020, vol. 30, no. 4, pp. 61-75.

A. V. Aho, M. R. Garey, J. D. Ullman, The transitive reduction of a directed graph. SIAM Journal on Computing, 1972, vol. 1, no. 2, pp. 131-137.

T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to algorithms. MIT press, 2009.

A. A. Grusho, E. E. Timonina, N. A. Grusho, I. Yu. Teryokhina, Identifying anomalies using metadata. Informatics and applications, 2020, vol. 14, no. 3, pp. 76-80.

Z. J. Czech, G. Havas, G., B.S. Majewski, An optimal algorithm for generating minimal perfect hash functions, Information processing letters, 1992, vol. 43, no. 5, pp. 257-264.

### Refbacks

- There are currently no refbacks.

Abava Absolutech Convergent 2020

ISSN: 2307-8162