THE PROBLEM OF COMPUTATIONAL COMPLEXITY IN BIG DATA ANALYSIS AND ALGORITHMIC APPROACHES TO ITS REDUCTION

Abstract

This article analyzes the main problems of computational complexity encountered in Big Data technologies and explores algorithmic solutions aimed at reducing them effectively. The study highlights challenges associated with the exponential growth of data volume, including the scarcity of time and memory resources, as well as issues related to parallel data processing and the use of distributed computing systems. Furthermore, the advantages of the MapReduce, Spark, and Approximation algorithms in mitigating computational complexity are examined through practical examples.