METHOD FOR DETECTING ANOMALOUS STATES OF A CONTROL OBJECT IN INFORMATION SYSTEMS BASED ON THE ANALYSIS OF TEMPORAL DATA AND KNOWLEDGE

Oksana Chala

Abstract


The problem of finding the anomalous states of the control object in the management information system under conditions of uncertainty caused by the incompleteness of knowledge about this object is considered. The method of classifying the current state of the control object in real time, allowing to identify the current anomalous state. The method uses temporal data and knowledge. Data is represented by sequences of events with timestamps. Knowledge is represented as weighted temporal rules and constraints. The method includes the following key phases: the formation of sequences of logical facts; selection of temporal rules and constraints; classification based on a comparison of rules and constraints. Logical facts are represented as predicates on event attributes and reflect the state of the control object. Logical rules define valid sequences of logical facts. Performing a classification by successive comparisons of constraints and weights of the rules makes it possible to more effectively identify the anomalous state since the comparison of the constraints reduces the subset of facts comparing to the current state. The method creates conditions for improving management efficiency in the context of incomplete information on the state of a complex object by using logical inference in knowledge bases for anomalous states of such control objects.


Keywords


anomalies; temporal rule; temporal knowledge base; management information system; event attributes

Full Text:

PDF

References


Alcamí, R. L., Carañana, C. D. (2012). Introduction to Management Information Systems. Universitat Jaume I, 39.

Sljivic, S., Skorup, S., Vukadinovic, P. (2015). Management control in modern organizations. International Review, 3-4, 39–49. doi: https://doi.org/10.5937/intrev1504039s

Dechow, N., Granlund, M., Mouritsen, J. (2006). Management Control of the Complex Organization: Relationships between Management Accounting and Information Technology. Handbooks of Management Accounting Research, 625–640. doi: https://doi.org/10.1016/s1751-3243(06)02007-4

Bubnicki, Z. (2005). Modern control theory. Berlin: Springer, 423. doi: https://doi.org/10.1007/3-540-28087-1

Kendal, S. L., Creen, M. (2007). An introduction to knowledge engineering. London: Springer, 290. doi: https://doi.org/10.1007/978-1-84628-667-4

Nakashole, N., Weikum, G. (2012). Real-time Population of Knowledge Bases: Opportunities and Challenges. Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX) AKBC Workshop, 41–45.

Kalynychenko, O., Chalyi, S., Bodyanskiy, Y., Golian, V., Golian, N. (2013). Implementation of search mechanism for implicit dependences in process mining. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS). doi: https://doi.org/10.1109/idaacs.2013.6662657

Galárraga, L., Heitz, G., Murphy, K., Suchanek, F. M. (2014). Canonicalizing Open Knowledge Bases. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management – CIKM ’14. doi: https://doi.org/10.1145/2661829.2662073

Richardson, M., Domingos, P. (2006). Markov logic networks. Machine Learning, 62 (1-2), 107–136. doi: https://doi.org/10.1007/s10994-006-5833-1

Singla, P., Domingos, P. (2006). Entity Resolution with Markov Logic. Sixth International Conference on Data Mining (ICDM’06). doi: https://doi.org/10.1109/icdm.2006.65

Kalynychenko, O., Chalyi, S., Bodyanskiy, Y., Golian, V., Golian, N. (2013). Implementation of search mechanism for implicit dependences in process mining. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS). doi: https://doi.org/10.1109/idaacs.2013.6662657

Sergii, C., Ihor, L., Aleksandr, P., Ievgen, B. (2018). Causality-based model checking in business process management tasks. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). doi: https://doi.org/10.1109/dessert.2018.8409176

Levykin, V., Chala, O. (2018). Method of automated construction and expansion of the knowledge base of the business process management system. EUREKA: Physics and Engineering, 4, 29–35. doi: http://dx.doi.org/10.21303/2461-4262.2018.00676

Levykin, V., Chala, O. (2018). Method of determining weights of temporal rules in markov logic network for building knowledge base in information control systems. EUREKA: Physics and Engineering, 5, 3–10. doi: http://dx.doi.org/10.21303/2461-4262.2018.00713

Levykin, V., Chala, O. (2018). Development of a method for the probabilistic inference of sequences of a business process activities to support the business process management. Eastern-European Journal of Enterprise Technologies, 5 (3 (95)), 16–24. doi: https://doi.org/10.15587/1729-4061.2018.142664

Gupta, M., Gao, J., Aggarwal, C. C., Han, J. (2014). Outlier Detection for Temporal Data: A Survey. IEEE Transactions on Knowledge and Data Engineering, 26 (9), 2250–2267. doi: https://doi.org/10.1109/tkde.2013.184




DOI: http://dx.doi.org/10.21303/2461-4262.2018.00787

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 Oksana Chala

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN 2461-4262 (Online), ISSN 2461-4254 (Print)