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Heterogeneous Integration of Big Data Using Semantic Web Technologies

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Intelligent Systems in Big Data, Semantic Web and Machine Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1344))

Abstract

Semantic web offers information for both individuals and computers to preserve large data scale semantically and provide a meaningful content of unstructured data. It offers new benefits for big-data research and applications. Big Data and Semantic Web are the epitome of computer sciences latest trend study subjects. Big data is a new tendency relates to a huge set of datasets including structured, semi-structured and unstructured data collected from different sources. Their integration faces many issues, as it is difficult to process this information using traditional databases and software methods. Recent works on the incorporation of both these technologies have provided a scalable approach in Data Analytics. This article attempts to give a comparative study of methods in integrating Big Data with Semantic Web, describing how Semantic Web makes Big Data smarter, revisits the difficulties and possibilities of Big Data and Semantic Web, and lastly summarizes the future direction of this inclusion.

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Author information

Authors and Affiliations

  1. Sultan Moulay Slimane University, ENSA Khouribga, Lasti Laboratory, Khouribga, Morocco

    Sajida Mhammedi & Noreddine Gherabi

Authors
  1. Sajida Mhammedi
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  2. Noreddine Gherabi
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Editor information

Editors and Affiliations

  1. ENSA, Sultan Moulay Slimane University, Khouribga, Morocco

    Noreddine Gherabi

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Mhammedi, S., Gherabi, N. (2021). Heterogeneous Integration of Big Data Using Semantic Web Technologies. In: Gherabi, N., Kacprzyk, J. (eds) Intelligent Systems in Big Data, Semantic Web and Machine Learning. Advances in Intelligent Systems and Computing, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-72588-4_12

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