Titlebook: Big Data Preprocessing; Enabling Smart Data Julián Luengo,Diego García-Gil,Francisco Herrera Book 2020 Springer Nature Switzerland AG 2020

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Imbalanced Data Preprocessing for Big Data,this divide-and-conquer strategy entails several problems, such as small disjuncts, data lack, etc. In this chapter we also review the latest proposals on imbalanced Big Data preprocessing and present a MapReduce framework for imbalanced preprocessing which includes several state-of-the-art sampling techniques.

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https://doi.org/10.1007/978-1-4614-5987-3pular frameworks in Big Data, and their main components. Next we also discuss other novel platforms for high-speed streaming processing that are gaining increasing importance in industry. Finally we make a comparison between two of the most relevant large-scale processing platforms nowadays: Spark and Flink.

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of some of the most recent solutions to address imbalanced This book offers a comprehensible overview of  Big Data Preprocessing, which includes a formal description of each problem.  It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms

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Book 2020e novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing

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