Logical integration of information systems based on expert systems

Objectives. The study set out to develop fundamental methodological principles for the logical integration of information systems (IS) in organizations and to quantitatively assess the topological significance of the IS integration process.

Methods. Methods based on expert systems were used for the logical integration of information in conjunction with data-mining approaches based on various IS. In order to quantitatively assess the topological significance of the IS integration procedure, graph theory methods were used. Discrete topology methods were also employed for calculating the topological invariants of the IS interconnection topology.

Results. Issues and challenges involved in the integration of IS in large organizations are considered in terms of integration methods based on physical and logical principles. While IS integration approaches based on logical principles offer distinct advantages over physical integration approaches, new problems arising in the context of logical integration approaches require innovative solutions. The proposed scheme for the logical integration of IS includes an algebraic method for quantitatively assessing the topological significance of integration, comprising an important numerical indicator in the logical integration of IS. Methods based on learning expert systems, which represent a fundamental solution for organizing the logical integration of IS for intelligent data analysis, are reviewed.

Conclusions. When integrating IS in organizations, it is advisable to use a logical integration approach that preserves the logic of existing information systems. The application of logical integration enables intelligent data analysis using various IS. The use of expert systems in logical integration enables the creation of a new logical layer for providing decision support within the organization.

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