AUTOMATION OF THE DATA PROCESSING PROCEDURE ON THE BASIS OF A STRONG SEMANTIC CONNECTION
Abstract and keywords
Abstract (English):
One of the defining tasks in automating the processing of current information obtained in the process of object research is the possibility of assigning it to specific objects located in the database (DB) of the information system. Such a task can be represented as a task of identifying the objects of analysis (monitoring) on current data. Initial information about a particular facility during the research can come from different sources and be incomplete and inaccurate. Due to identification, received data for each object are accumulated in the database for further automated analysis. But in a number of cases, the current information contains not specific names of the research objects of interest, but only some signs of their functioning: location, operating time, numerical characteristics of objects, etc. In such conditions, the identification of objects can occur on the basis of similarity of semantic features contained in the current information and attributes which have been already accumulated by the objects of research in the database. A distinctive feature of the subject area in which the analysis of objects is made is its variability; therefore, with the passage of time, the attributes serving to identify the objects may change. The addition of new features will result in a change in application software and will require the involvement of developers, which leads to additional time costs and decrease in the speed of procedure for identifying objects. This reason leads to the need to create a new approach to the automation of object monitoring, based on such requirements as high quality of the decisions made and ability to quickly change the logic of identification in the process of the system. The paper proposes the possibility of using predicate logic formulas, with the aim of making new information in the database more quickly and assigning it to a specific object, which makes it possible to develop the fastest solution.

Keywords:
classification, formalized representation, objects, expert systems
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В процессе мониторинга и автоматизированной обработки информации в БД системы анализируются объекты и связанных с ним данные. Каждый объект описывается набором характеристик и их числены значений, полученных в результате обработки и обобщения полученной информации. Используется двухуровневое представление данных по объектам мониторинга в БД информационной системы, под которым понимают только рассмотрение физического и логического уровней представления (рис. 1).

References

1. Gabets A.P. 1C:Predpriyatiye 8.1. Prostyye primery razrabotki [1C: Enterprise 8.1. Simple examples of de-velopment] / A.P. Gabets, Goncharov D.I. - Moscow: 1S- Publishing, 2015. - 383 r.

2. Borisov A.B. Bol'shoy ekonomicheskiy slovar' [The Big Economic Dictionary] / A.B. Borisov - 3rd ed., Pererab. and additional. - Moscow: ALGORITHM-BOOK, 2014 - 860 p.

3. Dubov Yu.A. Mnogokriterial'nyye modeli formirovaniya i vybora variantov sistem [Multi-criteria models for the formation and selection of variants of systems] / Yu.A. Dubov, S.I. Travkin, V.N. Yakimets. - Moscow: Nauka, 1986. - 65 p.

4. Dyukalov A.Ya. Teoriya upravleniya i ekonomicheskiye sistemy. Problemy opisaniya [Management Theory and Economic Systems. Problems of description] / A.Ya. Dyukalov, Yu.N. Ivanov, V.V. Tokarev // Automation and telemechanics, Moscow .: Deer - 2014. - № 12. - P. 111-119.

5. Ivanilov Yu.P. Matematicheskiye modeli v ekonomike [Mathematical models in economics] / Yu.P. Ivanilov, A.V. Lotov. - Moscow: Nauka, 2009. - 54 p.

6. Ivasenko AG Informatsionnyye tekhnologii v ekonomike i upravlenii: ucheb. posobiye [Information Tech-nologies in Economics and Management: Textbook. Allowance] / A.G. Ivasenko, A.Yu. Gridasov, V.A. Pavlenko. - 2 nd ed., Sr. - Moscow: KNORUS, 2011. - 160 p.

7. Sokolov A.V. Metodika otsenki effektivnosti poiska po nechetkim kharakteristikam v avtomatizirovannykh informatsionnykh sistemakh [A technique for evaluating the efficiency of searching for fuzzy characteristics in automated information systems] / A.V. Sokolov // Automation and modern technologies. - Moscow: Deer - 2010. - №3. - P. 25-27.

8. Khomichkov G.I. Identifikatsiya monitoringa po tekushchim dannym na osnove pravil bazy znaniy [Identification of monitoring on current data based on knowledge base rules / GI. Khomichkov] // Automation and modern technologies. - 2007. - №4. - P. 33-35.

9. Maklakov S.V. BPWin i ERWin. CASE-sredstva razrabotki informatsionnykh sistem [BPWin and ERWin. CASE-tools for the development of information systems] / S.V. Maklakov - Moscow.: Dialogue-MIFI, 2015. - 223 p.

10. Lemareshal C. New Variants of Bundle Methods / C. Lemareshal, A. Nemirovskii, Yu. Nesterov // Mathe-matical Programming. Serios B. - 2012. - V 69. - №1. - R.67-77.

11. Hertel S. Space sweep solves intersection of two convex polyhedron elegantly / S. Hertel, K. Mehlhorn, J. Nievergeit. - Acta Informatica, 21. - 2004. - P.501-519.

12. Johnston J. Econometric Methods / J. Johnston, J. DiNardo. N.Y.: The Mcgraw-Hill Companies, Inc., 1997. - 240 p.

13. McCreiqht E.M. An O(n loglog n)-time algorithm for triangulating a simple polygon / E.M. McCreiqht, C.J. van Wyk // SIAM J. Comput., 17 - 2008. - P. 143-178.

14. Rasmusen E. Games and information. An Introduction of Game Theory / E. Rasmusen. // N.Y.: Basil Blackwell Ltd, 1989. - R. 53-59


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