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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Introduction to Compiler Designramework that implemented the MapReduce paradigm. Apache Spark appeared a few years later improving the Hadoop Ecosystem. Similarly, Apache Flink appeared in the last years for tackling the Big Data streaming problem. However, as these frameworks were created for dealing with huge amounts of data, m

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https://doi.org/10.1007/978-0-85729-829-4nowledge and insights we can extract from it. Referring to the well-known “garbage in, garbage out” principle, accumulating vast amounts of raw data will not guarantee quality results, but poor knowledge. In this last chapter we aim to provide a couple of final thoughts on the importance of data pre

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Book 2020st relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. .This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and

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Introduction to Compiler Designitical impact in the learning process, as most learners suppose that the data is complete. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise and missing values, as they have difficulties coping with such a large amount of data.

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Introduction to Compiler Designthe early proposals on dealing with parallel discretization. Then, we present some distributed solutions capable of scaling on large-scale datasets. We finish with a study of the discretization methods capable of dealing with Big Data streams.

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