M.V. Vinogradova, A.S. Larionov, V.M. Chernenky

Bauman Moscow State Technical University (Moscow, Russia)

Currently, most modern manufacturing companies use automated information systems to account for resources and plan their activities. The functionality of these systems often becomes very extensive and actively changes over time. It is required to maintain the competence of employees when working with them, for which enterprise knowledge bases are created, which, however, themselves require significant labor costs to build.

This paper describes methods and tools for collecting and analyzing information about the actions of users in an automated enterprise management system, which allows you to identify problems in its business processes and the most frequent mistakes of employees.

Under the basic information system of the enterprise we mean the software complexes for resource management and control of production activities. One of the most common samples are products based on 1C: Enterprise platform. These systems include advanced functionality of the accounting of user actions, where the type and time of events is recorded, as well as instances of related objects. Common intelligent systems for automating the filling of knowledge bases concentrate on the generation of materials directly from the data structure of the main information system, or analysis of already existing documents in natural language. The approach we propose is not aimed at completely replacing a human expert, but at providing him with up-to-date information about the needs of users in the order of compiling knowledge base materials.

The information system of an enterprise is represented by a discrete dynamic system, whose elements change state as a result of user actions. Users' interactions with the information system are recorded as time-ordered sequences of elementary events, which are stored in a special logbook.

The subject area structure and business processes of the core information system are described in a knowledge base in the form of a graph structure. Its elements include human-readable materials and a set of metadata, allowing their analysis by algorithmic methods.

Based on the results of the work, an approach to identify the most relevant materials for the composition of the knowledge base materials is developed. It is based on algorithmic analysis of the actions of users of the basic information system and the calculation of the amount of time that users spend to eliminate emerging problems.

To assess the effectiveness of the proposed approach developed a simulation model and conducted experiments to estimate the downtime of users, depending on the method of filling the knowledge base.

Vinogradova M.V., Larionov A.S. Intelligent system for collecting and analyzing information about the actions of users of an automated enterprise management system. Dynamics of complex systems. 2021. T. 15. № 2. Р. 28−38. DOI: 10.18127/j19997493-20210203 (in Russian)

1. Alavi M., Leidner D.E. Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS quarterly. 2001. P. 107–136.
2. Gorlacheva E.N., Gudkov A.G., Omelchenko I.N., Drogovoz P.A., Koznov D.V. Knowledge Management Capability Impact on Enterprise Performance in Russian High-Tech Sector. In 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). 2018, June. P. 1–9).
3. Migdadi M.M., Abu Zaid M.K.S. An empirical investigation of knowledge management competence for enterprise resource planning systems success: insights from Jordan. International Journal of Production Research. 2016. V. 54(18). P. 5480–5498.
4. Mohapatra S., Agrawal A., Satpathy A. Designing knowledge management strategy. In Designing Knowledge Management-Enabled Business Strategies. 2016. P. 55–88. Springer, Cham.
5. 1C-Rarus. 1C:Enterprise platform. Business automation, consulting and support. https://rarus.ru/en. Accessed by 20 apr. 2019.
6. Vazhdaev A.N., Chernysheva T.Y., Lisacheva E.I. Software selection based on analysis and forecasting methods, practised in 1C. In IOP Conference Series: Materials Science and Engineering. 2015. V. 91. № 1. P. 012067. IOP Publishing.
7. Semantic MediaWiki. Semantic MediaWiki. http://mediawiki.org/wiki/Semantic_MediaWiki. Accessed by 20 apr. 2019.
8. 1C-Rarus (2019) Objects of configuration at 1C:Enterprise 8. https://its.1c.ru/db/metod8dev/content/2579/hdoc. Accessed by 20 apr. 2019.
9. Clifton B. Advanced web metrics with Google Analytics. John Wiley & Sons. 2012.
10. Aggarwal C.C. Data mining: the textbook. Springer. 2015.
11. Fedotova A.V., Tabakov V.V., Ovsyannikov M.V., Bruening J. Ontological Modeling for Industrial Enterprise Engineering. In International Conference on Intelligent Information Technologies for Industry. 2018. September. P. 182–189. Springer, Cham.
12. Chernenkiy V., Gapanyuk Y., Terekhovl V., Revunkov, G., & Kaganov, Y. The hybrid intelligent information system approach as the basis for cognitive architecture. Procedia computer science. 2018. V. 145. P. 143–152.
13. Kanev A., Cunningham S., Valery T. Application of formal grammar in text mining and construction of an ontology. In 2017 Internet Technologies and Applications (ITA). 2017. September. P. 53–57.
14. Chernenkiy V.M., Gapanyuk Y.E., Kaganov Y.T., Dunin I.V., Lyaskovsky M.A., Larionov V.S. Storing Metagraph Model in Relational, Document-Oriented, and Graph Databases. 2018.
15. Alfimtsev A.N., Loktev D.A., Loktev A.A. Comparison of Development Methodologies for Systems of Intellectual Interaction. In Proceedings of Moscow State University of Civil Engineering. 2013. № 5. P. 200–208.
16. Barbara D., Kamath C. (Eds.). Proceedings of the 2003 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. 2003.
17. Cook J.E., Du Z., Liu C., Wolf A.L. Discovering models of behavior for concurrent workflows. Computers in industry. 2004. V. 53(3). P. 297–319.