On Systematic Approach to Discovering Periodic Patterns in Event Logs

  • Marcin ZimniakEmail author
  • Janusz R. GettaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)


Discovering periodic patterns from historical information is a computationally hard problem due to the large amounts of historical data to be analyzed and due to a high complexity of the patterns. This work shows how the derivations rules for periodic patterns can be applied to discover complex patterns in case of logs of events. The paper defines a concept of periodic pattern and its validation in a workload trace created from the logs of events. A system of derivations rules that transforms periodic patterns into the logically equivalent ones is proposed. The paper presents a systematic approach based on the system of derivation rules to discovery of periodic patterns in logs of events.


Periodic Pattern Composition Rule Discovery Rule Derivation Rule Split Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Van der Aalst, W.M.P.: Process Mining Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)Google Scholar
  2. 2.
    Luna, J., Cano, A., Sakalauskas, V., Ventura, S.: Discovering useful patterns from multiple instance data. Inf. Sci. 357, 23–38 (2016)CrossRefGoogle Scholar
  3. 3.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Disc. 1, 259–289 (1997)CrossRefGoogle Scholar
  4. 4.
    Özden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: Proceedings of the Fourteenth International Conference on Data Engineering, pp. 412–421 (1998)Google Scholar
  5. 5.
    Rasheeed, F., Alshalalfa, M., Alhajj, R.: Efficient periodicity mining in time series databases using suffix trees. IEEE Trans. Knowl. Data Eng. 23(1), 79–94 (2011)CrossRefGoogle Scholar
  6. 6.
    Huang, K.Y., Chang, C.H.: SMCA: A general model for mining asynchronous periodic patterns in temporal databases. IEEE Trans. Knowl. Data Eng. 17(6), 774–785 (2005)CrossRefGoogle Scholar
  7. 7.
    Yeh, J.S., Lin, S.C., Hu, S.C.: Novel algorithms for asynchronous periodic pattern mining based on 2-d linked list. Int. J. Database Theory Appl. 5(4), 33–43 (2012)Google Scholar
  8. 8.
    Getta, J., Zimniak, M., Benn, W.: Mining periodic patterns from nested event logs. In: The 14th IEEE International Conference on Computer and Information Technology, CIT 2014, pp. 160–167 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Computer ScienceTU ChemnitzChemnitzGermany
  2. 2.School of Computer Science and Software EngineeringUniversity of WollongongWollongongAustralia

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