Methods of aggregation, size reduction and big data processing
Abstract
Methods of aggregation, size reduction and big data processing
Incoming article date: 02.11.2021The relevance of the study is due to the fact that big data analysis can be problematic, since it often involves the collection and storage of mixed data that are based on different rules or patterns. In this regard, the goal of this article is analyzing existing methods of big data processing that can be applied to the processing of mixed or heterogeneous data. The article describes the advantages and disadvantages of the most commonly used methods of processing mixed data. The problems of processing heterogeneous data are revealed. Big data processing tools, some traditional methods of data mining, as well as machine learning are presented. The advantages of merging large mixed data are presented. In this paper, heterogeneous data should be understood as any data with high variability of data types, formats and nature of origin. The materials of the article have a practical value for big data processing, the choice of big data processing methods, including data cleaning, data aggregation, size reduction and processing of mixed data and related analytical and system analysis.
Keywords: heterogeneous data, mixed data, multi-scale data, data processing methods, data mining, data analytics