https://oldena.lpnu.ua/handle/ntb/45127| Title: | Models of temporal dependencies for a probabilistic knowledge base |
| Authors: | Chala, O. |
| Affiliation: | Kharkiv National University of Radio Electronics |
| Bibliographic description (Ukraine): | Chala O. Models of temporal dependencies for a probabilistic knowledge base / O. Chala // Econtechmod. — Lublin, 2018. — Vol 7. — No 3. — P. 53–58. |
| Bibliographic description (International): | Chala O. Models of temporal dependencies for a probabilistic knowledge base / O. Chala // Econtechmod. — Lublin, 2018. — Vol 7. — No 3. — P. 53–58. |
| Is part of: | Econtechmod, 3 (7), 2018 |
| Journal/Collection: | Econtechmod |
| Issue: | 3 |
| Volume: | 7 |
| Issue Date: | 18-Jun-2018 |
| Place of the edition/event: | Lublin |
| Keywords: | temporal dependencies temporal rule knowledge base information control system event attribute event log |
| Number of pages: | 6 |
| Page range: | 53-58 |
| Start page: | 53 |
| End page: | 58 |
| Abstract: | The article presents models of temporal dependences for constructing probabilistic temporal rules in the Markov Logical Networks. Such rules describe the relations between the states of a control object and taking account the possibility of integrating different approaches of management according to the paradigm of “Enterprise 2.0” knowledge sharing. The proposed models define constraints and conditions for changing the states of a control object, which allows predicting possible variants of its behavior in relation to the current state and providing decision support based on a choice of the most likely variants. |
| URI: | https://ena.lpnu.ua/handle/ntb/45127 |
| ISSN: | 2084-5715 |
| Copyright owner: | © Copyright by Lviv Polytechnic National University 2018 © Copyright by Polish Academy of Sciences 2018 © Copyright by University of Engineering and Economics in Rzeszów 2018 © Copyright by University of Life Sciences in Lublin 2018 |
| URL for reference material: | https://doi |
| References (Ukraine): | 1. Bughin J. 2008. The rise of enterprise 2.0. Journal of Direct, Data and Digital Marketing Practice, 9(3),251–259. 2. 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. Available:https://doi. org/10.1109/idaacs.2013.6662. 3. Christidis K., Mentzas G., Apostolou D. 2012. Using latent topics to enhance search andrecommendation in Enterprise Social Software. Expert Systems with Applications, 39(10), 9297–9307. 4. Vom Brocke J. 2015. Handbook on Business Process Management 1. Introduction, Methods, and Information Systems. Springer-Verlag Berlin Heidelberg, p. 709 doi:10.1007/978-3-642-45100-3 5. Shin J., Wu S., Wang F., De Sa C. Zhang С., R´e С. 2015. Incremental Knowledge Base Construction Using DeepDive. 41 th International Conference on Very Large Data Bases (VLDB).Vol. 8(11). 6. Niu F., Zhang C., Re C. 2012. DeepDive: Webscale Knowledge-base Construction using Statistical Learning and Inference. VLDS, 25–28. 7. Chalyi S., Levykin I., Petrychenko A. and Bogatov I. 2018. Causality-based model checking in business process management tasks. Proc. IEEE 9th International Conference on Dependable Systems, Services and Technologies DESSERT’2018. Ukraine, Kyiv. May 24–27, 478 – 483. 8. Van der Aalst W. M. P. 2014. Process Mining in the Large. A Tutorial. Business Intelligence. Springer Science + Business Media, 33–76.doi:10.1007/978-3-319-05461-2_2 9. Gronau N., Thim C., Ullrich A., Weber E. 2016. A Proposal to Model Knowledge in Knowledge- Intensive Business Processes. BMSD 2016: 6th Int. Symposium on Business Modeling and Software Design. doi:10.5220/0006222600980103. 10. Richardson M., Domingos P. 2006. Markov logic networks. Machine learning, 62(1-2), 107–136.doi: 10.1007/s10994- 006-8633-8. 11. Lowd D., Domingos P. 2007. Efficient weight learning for Markov logic networks. European Conference on Principles of Data Mining and Knowledge Discovery. Knowledge discovery in databases: PKDD 2007. 12. 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. 13. Levykin V., Chala O. 2018. Method of determining weights of temporal rules in markov logic network for building knowledge base in information control system. EUREKA: Physics and Engineering, 5,29–35. 14. Christian W. Gunther, Eric Verbeek. 2014. XES Standard Definition. 24. 15. Beskorovainyi V. V., Berezovskyi H. 2017. Identification of preferences in decision support systems. Econtechmod. An international quarterly journal. Vol. 6, No. 4, 15–20. |
| References (International): | 1. Bughin J. 2008. The rise of enterprise 2.0. Journal of Direct, Data and Digital Marketing Practice, 9(3),251–259. 2. 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. Available:https://doi. org/10.1109/idaacs.2013.6662. 3. Christidis K., Mentzas G., Apostolou D. 2012. Using latent topics to enhance search andrecommendation in Enterprise Social Software. Expert Systems with Applications, 39(10), 9297–9307. 4. Vom Brocke J. 2015. Handbook on Business Process Management 1. Introduction, Methods, and Information Systems. Springer-Verlag Berlin Heidelberg, p. 709 doi:10.1007/978-3-642-45100-3 5. Shin J., Wu S., Wang F., De Sa C. Zhang S., R´e P. 2015. Incremental Knowledge Base Construction Using DeepDive. 41 th International Conference on Very Large Data Bases (VLDB).Vol. 8(11). 6. Niu F., Zhang C., Re P. 2012. DeepDive: Webscale Knowledge-base Construction using Statistical Learning and Inference. VLDS, 25–28. 7. Chalyi S., Levykin I., Petrychenko A. and Bogatov I. 2018. Causality-based model checking in business process management tasks. Proc. IEEE 9th International Conference on Dependable Systems, Services and Technologies DESSERT’2018. Ukraine, Kyiv. May 24–27, 478 – 483. 8. Van der Aalst W. M. P. 2014. Process Mining in the Large. A Tutorial. Business Intelligence. Springer Science + Business Media, 33–76.doi:10.1007/978-3-319-05461-2_2 9. Gronau N., Thim C., Ullrich A., Weber E. 2016. A Proposal to Model Knowledge in Knowledge- Intensive Business Processes. BMSD 2016: 6th Int. Symposium on Business Modeling and Software Design. doi:10.5220/0006222600980103. 10. Richardson M., Domingos P. 2006. Markov logic networks. Machine learning, 62(1-2), 107–136.doi: 10.1007/s10994- 006-8633-8. 11. Lowd D., Domingos P. 2007. Efficient weight learning for Markov logic networks. European Conference on Principles of Data Mining and Knowledge Discovery. Knowledge discovery in databases: PKDD 2007. 12. 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. 13. Levykin V., Chala O. 2018. Method of determining weights of temporal rules in markov logic network for building knowledge base in information control system. EUREKA: Physics and Engineering, 5,29–35. 14. Christian W. Gunther, Eric Verbeek. 2014. XES Standard Definition. 24. 15. Beskorovainyi V. V., Berezovskyi H. 2017. Identification of preferences in decision support systems. Econtechmod. An international quarterly journal. Vol. 6, No. 4, 15–20. |
| Content type: | Article |
| Appears in Collections: | Econtechmod. – 2018. – Vol. 7, No. 3 |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018v7n3_Chala_O-Models_of_temporal_dependencies_53-58.pdf | 182.12 kB | Adobe PDF | View/Open | |
| 2018v7n3_Chala_O-Models_of_temporal_dependencies_53-58__COVER.png | 451.39 kB | image/png | View/Open |
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