online read us now
Paper details
Number 4 - December 2021
Volume 31 - 2021
Applications of rough sets in big data analysis: An overview
Piotr Pięta, Tomasz Szmuc
Abstract
Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial
applications. Processing massive amounts of data generated by the IoT and stored in distributed space is not a straightforward
task and may cause many problems. During the last few decades, scientists have proposed many interesting
approaches to extract information and discover knowledge from data collected in database systems or other sources. We
observe a permanent development of machine learning algorithms that support each phase of the data mining process,
ensuring achievement of better results than before. Rough set theory (RST) delivers a formal insight into information,
knowledge, data reduction, uncertainty, and missing values. This formalism, formulated in the 1980s and developed by
several researches, can serve as a theoretical basis and practical background for dealing with ambiguities, data reduction,
building ontologies, etc. Moreover, as a mature theory, it has evolved into numerous extensions and has been transformed
through various incarnations, which have enriched expressiveness and applicability of the related tools. The main aim of
this article is to present an overview of selected applications of RST in big data analysis and processing. Thousands of
publications on rough sets have been contributed; therefore, we focus on papers published in the last few years. The applications of RST are considered from two main perspectives: direct use of the RST concepts and tools, and jointly with other approaches, i.e., fuzzy sets, probabilistic concepts, and deep learning. The latter hybrid idea seems to be very promising
for developing new methods and related tools as well as extensions of the application area.
Keywords
rough sets theory, big data analysis, deep learning, data mining, tools