Combinatorial Explosion Problem of Big Data Analytics by Morphological Approach

Decision making by extracting the knowledge underlying Big Data is considered. Big Data and morphological synthesis of engineering systems can be interrelated in the context of using Big Data to improve and develop innovative systems. The analysis and use of Big Data can help to identify patterns, trends and relationships between different components of technical systems and optimize their performance. Through the use of Big Data, morphological synthesis of engineering solutions can be more accurate and efficient, contributing to the development of new technologies and innovations in various fields. However, one of the major challenges in morphological systems analysis is the curse of dimensionality or combinatorial explosion. The paper discusses possible approaches to reduce the dimensionality of the problem. In particular, it is shown that the application of clustering and variant selection can reduce the number of analyzed variants and select the best ones. The proposed approach allows to increase the efficiency of search for rational engineering solutions. Unlike AI on neural networks, the solutions based on the morphological approach are transparent and controllable. The approach used can improve the validity and visibility of the information analyzed and increase the automation of processes in design and production.