Big data analytics correlation taxonomy

Husamaldin, Laden and Saeed, Nagham ORCID: (2019) Big data analytics correlation taxonomy. Information, 11 (1). p. 17.

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Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries.

Item Type: Article
Identifier: 10.3390/info11010017
Additional Information: ** From MDPI via Jisc Publications Router ** History: accepted 22-12-2019; pub-electronic 25-12-2019. ** Licence for this article:
Keywords: big data analytics methods, big data characteristics, big data techniques, big data correlation taxonomy
Subjects: Computing > Information management
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SWORD Depositor: Jisc Router
Depositing User: Jisc Router
Date Deposited: 03 Jan 2020 11:12
Last Modified: 06 Feb 2024 16:01


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