Applications of Rough Sets in Big Data Analysis: An Overview
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 t...
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| Format: | Article |
| Language: | English |
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Sciendo
2021-12-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
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| Online Access: | https://doi.org/10.34768/amcs-2021-0046 |
| _version_ | 1818082852838834176 |
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| author | Pięta Piotr Szmuc Tomasz |
| author_facet | Pięta Piotr Szmuc Tomasz |
| author_sort | Pięta Piotr |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T19:28:41Z |
| format | Article |
| id | doaj.art-005e4a19c6f54de3a396e47394f9009e |
| institution | Directory Open Access Journal |
| issn | 2083-8492 |
| language | English |
| last_indexed | 2024-12-10T19:28:41Z |
| publishDate | 2021-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj.art-005e4a19c6f54de3a396e47394f9009e2022-12-22T01:36:18ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922021-12-0131465968310.34768/amcs-2021-0046Applications of Rough Sets in Big Data Analysis: An OverviewPięta Piotr0Szmuc Tomasz1Department of Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059Kraków, PolandDepartment of Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059Kraków, PolandBig 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.https://doi.org/10.34768/amcs-2021-0046rough sets theorybig data analysisdeep learningdata miningtools |
| spellingShingle | Pięta Piotr Szmuc Tomasz Applications of Rough Sets in Big Data Analysis: An Overview International Journal of Applied Mathematics and Computer Science rough sets theory big data analysis deep learning data mining tools |
| title | Applications of Rough Sets in Big Data Analysis: An Overview |
| title_full | Applications of Rough Sets in Big Data Analysis: An Overview |
| title_fullStr | Applications of Rough Sets in Big Data Analysis: An Overview |
| title_full_unstemmed | Applications of Rough Sets in Big Data Analysis: An Overview |
| title_short | Applications of Rough Sets in Big Data Analysis: An Overview |
| title_sort | applications of rough sets in big data analysis an overview |
| topic | rough sets theory big data analysis deep learning data mining tools |
| url | https://doi.org/10.34768/amcs-2021-0046 |
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