![]() ![]() Big data analytic and drought are introduced and reviewed in this paper. In fact, big data handle data heterogeneity which is an additive value for the prediction of drought, it offers a view of the different dimensions such as the spatial distribution, the temporal distribution and the severity detection of this phenomenon. There are numerous emerging studies addressing big data and its applications in drought monitoring. For this reason, early warning, accurate evaluation, and efficient prediction are an emergency especially for the nations that are the most menaced by this danger. It threatens agricultural production, ecological environment, and socio-economic development. Droughts stand among the most damaging natural disasters. Consequently, drought monitoring using big data analytic has gained an explosive interest. Over recent years, the frequency and intensity of droughts have increased and there has been a large drying trend over many parts of the world. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. The available big data analytics tools and platforms are categorized. The paper also highlights a systematic review of available big data techniques and analytics. Initially, an overview of big data content, key characteristics, and related topics are presented. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. ![]() Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. Furthermore, 2.78% studies have proposed approaches oriented to hybrid databases with a real case for structured, semi-structured and unstructured data.īig data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. For instance, Entity Relationship and document-oriented are the most researched models at the conceptual and logical abstraction level respectively and MongoDB is the most frequent implementation at the physical. Moreover, we present a complete bibliometric analysis in order to provide detailed information about the authors and the publication data in a single document. As result, 36 studies, collected from the most important scientific digital libraries and covering the period between 20, were deemed relevant. Finally, the third question determines what trends and gaps exist according to three key concepts: the data source, the modeling and the database. The second question is whether the research is focused on semi-structured and/or unstructured data and what techniques are applied. The first question is how the number of published papers about Big Data modeling and management has evolved over time. This study answers three research questions. The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. ![]()
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