Creation of intelligent information decision support systems

Abstract

The use of intelligent information decision support systems implies considering the problem area's specifics. The object of study is characterized by the following set of features: - quality and efficiency of decision-making; - vagueness of goals and institutional boundaries; - the plurality of subjects involved in solving the problem; - randomness; - a plurality of mutually influencing factors; - weak formalizability, uniqueness of situations; - latency, concealment, the implicitness of information. For the efficient and reliable functioning of agricultural facilities and enterprises, it is necessary to create and implement intelligent information systems. Over the past quarter of a century, domestic information systems have undergone a progressive evolution, both in terms of developing the theoretical principles of their construction and implementing these systems. The restructuring of agriculture, the market conditions for the functioning of objects, and agriculture enterprises have their characteristics and problems. Building the structure of intelligent decision support information systems is primarily associated with building a system model, in which both traditional elements of the control system and knowledge processing models should be defined. To solve these problems, methods of system analysis were used. The key research method is the optimization of data representation structures of databases and knowledge. The following relational data representation structures have been identified: relations, attributes, and values. In the relational model, structures are not specially allocated to represent data about entity relationships. Semantic networks use a three-level representation of data on entities and a four-level representation of data on entity relationships. The conducted studies have shown that in data representation structures, entity-relationship models are a generalization and development of the structures of all traditional data models since only in this data model there are 4-level data representations of both entities and relationships. All other traditional models are some special cases of the most general entity-relationship model.