The approach of granular computing and rough sets for identifying situations

• Autorzy: Burov Ye. , Mykich K.
• Języki publikacji: EN

Abstrakty

EN

In the article are described problems related to creation and maintenance of situational awareness systems. The definitions of concepts of situation and its identification are presented. An approach based on situational knowledge representation with ontological models is selected for attaining situational awareness in complex intelligent enterprise systems, where objects can be in several situations in the same time and some situations are defined imprecisely. Granular computing approach is used for reduction of situational knowledge management complexity. In order to work with situation defined imprecisely, rough set approximations are proposed for situation definition. The usage of mechanisms inherent to ontological modeling for situation representation and reasoning about them are also discussed.

Słowa kluczowe

EN

situation; situational awareness system; ontology; situational assessment; granular computing; rough sets theory

PL

koniunktura; system świadomości sytuacji; ontologia; ocena sytuacji; obliczenia ziarniste; teoria zbiorów przybliżonych

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2017
• Tom: Vol. 6, No 2
• Strony: 45--50
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Burov Ye.

• Polytechnic National University

autor

Mykich K.

• Polytechnic National University

Bibliografia

• 1. Lytvyn V. 2013. Design of intelligent decision support systems using ontological approach. ECONTECHMOD. An international quarterly journal, Vol. 2, No. 1, рр. 31–37.
• 2. Burov Y. and Mykich K. 2016. Algebraic model for knowledge representation in situational awareness systems. XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT). Lviv Polytechnic Publishing House, рр. 165–167.
• 3. Burov Y. and Horodetska A. 2010. Intellectual tourist service for processing context of situation. Lviv National Polytechnic University, 689, рр. 27–35 (in Ukrainian).
• 4. Durso F. T. and Gronlund S. D. 1999. Situation awareness: Handbook of applied cognition. University of Oklahoma, рр. 283–314.
• 5. Reiter R. 2001. Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press Publisher, 448.
• 6. Situation. Cambridge dictionary online. Available online at:<http://dictionary.cambridge.org/dictionary/english/situation>.
• 7. Devlin K. 2006. Situation theory and situation semantics: handbook of the History of Logic, Vol. VII, 601–664.
• 8. Settembre G. P. 2007. Multi-agent approach to Situation Assessment. University “Sapienza” of Rome, 1–106.
• 9. Burov Y. 2014. Business process modelling using ontological task models. ECONTECHMOD. An international quarterly journal, Vol. 1, No. 1, рр. 11–22.
• 10. Chornous G. 2012. Identification mechanism of situations and causal relationships between events at an enterprise. Taras Shevchenko National University of Kyiv, рр. 15–20 (in Ukrainian).
• 11. Savchuk T.O. and Petryshyn S. I. 2014. Problem situations and their states identification in complex technical systems using modified forel algorithm. Lviv National Polytechnic University, No. 783, рр. 187–192 (in Ukrainian).
• 12. Savchuk T. O. and Petryshyn S. I. 2012. Evaluation of the simulation of cluster analysis of emergencies of railways. Vinnytsia National Technical University, No. 1, рр. 18–24 (in Ukrainian).
• 13. Lytvyn V., Semotuyk O. and Moroz O. 2013. Definition of the semantic metrics on the basis of thesaurus of subject area. ECONTECHMOD. An international quarterly journal on economics in technology, new technologies and modelling processes, Vol. IІ, No. 4, рр. 47–52.
• 14. Endsley M. and Mica R. 2000. Theoretical underpinnings of situation awareness: a critical review Process More Data ≠ More Information. Edited byArray. Most, Vol. 301, рр. 3–2.
• 15. Burov Y. and Mykich K. 2016. Algebraic Framework for Knowledge Processing in Systems with Situational Awareness. Advances in Intelligent Systems and Computing. Springer International Publishing, рр. 217–227.
• 16. Steinberg A. N., Bowman C. L. and White F. E. 1999. Revisions to the JDLModel. In: Sensor Fusion: Architectures, Algorithms, and Applications, Proceedings of the SPIE, Vol. 3719, рр. 430–441.
• 17. Siedushev O. and Burov Y. 2014. Methods for data mining from fuzzy knowledge bases. Lviv National Polytechnic University, No. 783, рр. 193–203 (in Ukrainian).
• 18. Yao Y. Y. 2001. Information granulation and rough set approximation. International Journal of Intelligent Systems, Vol. 16, No. 1, рр. 87–104.
• 19. Minaiev Y., Filimonova O. and Minaieva Y. 2015. Structured fs-granules in problems of granular computing. Electronic modeling, Vol. 37, No. 1, рр. 77–95 (in Russian).
• 20. Yao J. T. 2005. Information Granulation and Granular Relationships. In Proceedings of the IEEEConference on Granular Computing, pp. 326–329.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).