Peculiarities of associative data processing in intelligent systems

Associative operations are computational massively parallel procedures over large data sets. This explains their widespread use in such application areas as database management systems (DBMS), searching and sorting IP addresses in computer networks, and ranking data, for example, in decision-making subsystems as part of intelligent systems, in particular, for medical diagnostics. This is due, not least, to the fact that associative operations include selection by foreign key, searching for data by analogy, sorting and ranking of elements of a data set. This paper presents the results of an analysis of the features of the application of associative data processing methods for solving problems in intelligent systems. The definition of intelligent memory is considered as one that is expanded due to the functional capabilities of associative memory, i.e. memory with content-addressing. In this case, associative data processing includes not only a search by association, that is, by a foreign key, but also a search for an extreme (maximum/minimum) element in a numerical array. Another example of the application of associative data processing are varieties of neural networks that perform the functions of auto- and heteroassociative memory. The use of neural networks in intelligent control systems of mobile robots is especially relevant today, since their structure is provided by associative processing levels. Another popular approach is the use of a classifier with extended functional capabilities as part of decision support subsystems for expert systems for various purposes. These examples indicate a specific connection between associative data processing methods and the implementation of neurotechnologies in the creation of intelligent systems for various purposes.