JSS  Vol.3 No.11 , November 2015
Discovering Complex Incomplete Periodic Patterns through Logical Derivations
ABSTRACT

Discovering complex and incomplete periodic patterns in the logs of events is a complicated and time consuming task. This work shows that it is possible to discover complex and incomplete periodic patterns through finding simple patterns first and through logical derivations of complex and incomplete patterns later on. The paper defines a syntax and semantics of a class of periodic patterns that frequently occur in the logs of events. A system of derivation rules proposed in the paper can be used to transform a set of periodic patterns into a logically equivalent set of patterns. The rules are used in the algorithms that derive complex and incomplete periodic patterns. A prototype implementation of the algorithms that discover complex and incomplete periodic patterns in the logs of events is presented.


Cite this paper
Getta, J. and Zimniak, M. (2015) Discovering Complex Incomplete Periodic Patterns through Logical Derivations. Open Journal of Social Sciences, 3, 8-15. doi: 10.4236/jss.2015.311002.
References
[1]   Bruno, N. (2011) Automated Physical Database Design and Tuning. CRC Press Taylor and Francis Group, Boca Raton.

[2]   Van der Alst, W.M.P. (2011) Process Mining Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin.

[3]   Mannila, H., Toivonen, H. and Verkamo, A.I. (1997) Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery, 1, 259-289. http://dx.doi.org/10.1023/A:1009748302351

[4]   Wojciechowski, M. (2000) Discovering Frequent Episodes in Se-quences of Complex Events. Proceedings of Enlarged Fourth East-European Conference on Advances in Databases and Information Systems (ADBIS-DASFAA), 205-214.

[5]   Ozden, B., Ramaswamy, S. and Silberschatz, A. (1998) Cyclic Association Rules. Proceedings of the Fourteenth International Conference on Data Engineering, 412-421. http://dx.doi.org/10.1109/ICDE.1998.655804

[6]   Rasheeed, F., Alshalalfa, M. and Alhajj, R. (2011) Efficient Peri-odicity Mining in Time Series Databases Using Suffix Trees. IEEE Transactions on Knowledge and Data Engineering, 23, 79-94. http://dx.doi.org/10.1109/TKDE.2010.76

[7]   Huang, K.-Y. and Chang, C.-H. (2004) Asynchronous Peri-odic Patterns Mining in Temporal Databases. Databases and Applications, IASTED/ACTA Press, 43-48.

[8]   Yang, J., Wang, W. and Yu, P.S. (2003) Mining Asynchronous Periodic Patterns in Time Series Data. IEEE Transactions on Knowledge and Data Engineering, 15, 613-628. http://dx.doi.org/10.1109/TKDE.2003.1198394

[9]   Yeh, J.-S., Lin, S.-C. and Hu, S.-C. (2013) Novel Algorithms for Asynchronous Periodic Pattern Mining Based on 2-d Linked List. In-ternational Journal of Database Theory and Application, 5, 33-43.

[10]   Zimniak, M., Getta, J. and Benn, W. (2014) Deriving Composite Periodic Patterns from Database Audit Trails. The 6th Asian Conference on Intelligent Information and Database Systems, 310-321. http://dx.doi.org/10.1007/978-3-319-05476-6_32

[11]   Getta, J., Zimniak, M. and Benn, W. (2014) Mining Periodic Patterns from Nested Event Logs. The 14th IEEE International Conference on Computer and Information Technology, Xi’an, 160-167. http://dx.doi.org/10.1109/cit.2014.27

[12]   Simovici, D.A. and Djeraba, C. (2008) Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics. Advanced Information and Knowledge Processing, Springer.

[13]   Zimniak, M., Getta, J.R. and Benn, W. (2014) Discovering Periodic Patterns in System Logs. Proceed-ings of the LWA 2014 Workshops: KDML, IR, FGWM, Aachen, 156-161.

 
 
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