LEADER 04239nam 22006015 450 001 996465345703316 005 20200702153750.0 010 $a3-030-39105-1 024 7 $a10.1007/978-3-030-39105-8 035 $a(CKB)4100000010673048 035 $a(DE-He213)978-3-030-39105-8 035 $a(MiAaPQ)EBC6138227 035 $a(PPN)243229011 035 $a(EXLCZ)994100000010673048 100 $a20200316d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig Data Preprocessing$b[electronic resource] $eEnabling Smart Data /$fby Julián Luengo, Diego García-Gil, Sergio Ramírez-Gallego, Salvador García, Francisco Herrera 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIII, 186 p. 57 illus., 54 illus. in color.) 311 $a3-030-39104-3 320 $aIncludes bibliographical references. 327 $a1. Introduction -- 2. Big Data: Technologies and Tools -- 3. Smart Data -- 4. Dimensionality Reduction for Big Data -- 5. Data Reduction for Big Data -- 6. Imperfect Big Data -- 7. Big Data Discretization -- 8. Imbalanced Data Preprocessing for Big Data -- 9. Big Data Software -- 10. Final Thoughts: From Big Data to Smart Data.-. 330 $aThis 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 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 to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some 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 techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book. 606 $aBig data 606 $aMachine learning 606 $aComputers 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 615 0$aBig data. 615 0$aMachine learning. 615 0$aComputers. 615 14$aBig Data. 615 24$aMachine Learning. 615 24$aInformation Systems and Communication Service. 676 $a005.7 700 $aLuengo$b Julián$4aut$4http://id.loc.gov/vocabulary/relators/aut$0720993 702 $aGarcía-Gil$b Diego$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aRamírez-Gallego$b Sergio$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGarcía$b Salvador$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHerrera$b Francisco$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465345703316 996 $aBig Data Preprocessing$92143221 997 $aUNISA