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Usmanova I. V., Korovina L.V. On the development of an automated information system of document flow analysis

Published in journal "Software systems and computational methods", 2014-1 in rubric "Knowledge Base, Intelligent Systems, Expert Systems, Decision Support Systems", pages 63-69.

Resume: the article is devoted to solving a problem of analysis of document flow in organization. Constant improvement of production process leads to intense modification of structure and amount of produced documentations and their flow ways and requires continuous studying of document flow. Currently there is no unified and universal technique of document flow analysis. Comprehensive analysis of the document flow is highly labor-consuming and requires considerable effort in collecting, systematization and analysis of data. Therefore the author concludes that there is a need to automate processes of gathering and analyzing data. The weakness interconnection between parameters of document flow and business processes evaluation makes it appropriate to use the techniques of knowledge engineering. The paper reviews multilevel model of knowledge representation, including semantic network of concepts, statements, business-processes and forecasts. Since all characteristics of document flow and processes are probabilistic, the authors suggest to use Bayesian method for system adaptation to the characteristics of the current situation. The proposed system allows to study current state of document flow in given period of time and provides an opportunity to forecast not only the bottlenecks in the document flow, but also predict indicators of organizational performance, depending on the characteristics of the state of document flow. As a result the use of AIS allows to bring both document flow analysis and functioning of the organization as a whole to a completely new level.

Keywords: document, document flow, business process, automated informational system, database, base of knowledge, knowledge representation model, semantic network, adaptation, Bayesian treatment

DOI: 10.7256/2305-6061.2014.1.11398

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