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Data science and big data = an envir...

Pedrycz, Witold.

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Data science and big data = an environment of computational intelligence /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science and big data/ edited by Witold Pedrycz, Shyi-Ming Chen.
Reminder of title:	an environment of computational intelligence /
other author:	Pedrycz, Witold.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	viii, 303 p. :ill., digital ;24 cm.
[NT 15003449]:	Part I. Fundamentals -- Large-Scale Clustering Algorithms -- On High Dimensional Search Space and Learning Methods -- Enhanced Over_Sampling Techniques for Imbalanced Big Data Set Classification -- Online Anomaly Detection in Big Data: The First Line of Defense Against Intruders -- Developing Modified Classifier for Big Data Paradigm: An Approach through Bio-Inspired Soft Computing -- Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing of Big Data -- An Efficient Approach for Mining High Utility Itemsets over Data Streams -- Event Detection in Location-Based Social Networks -- Part II. Applications -- Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey -- Big Data for Effective Management of Smart Grids -- Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics -- Predicting Spatiotemporal Impacts of Weather on Power Systems using Big Data Science -- Index.
Contained By:	Springer eBooks
Subject:	Computational intelligence. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-53474-9
ISBN:	9783319534749

Data science and big data = an environment of computational intelligence /
Data science and big dataan environment of computational intelligence /[electronic resource] :edited by Witold Pedrycz, Shyi-Ming Chen. - Cham :Springer International Publishing :2017. - viii, 303 p. :ill., digital ;24 cm. - Studies in big data,v.242197-6503 ;. - Studies in big data ;v.24..

Part I. Fundamentals -- Large-Scale Clustering Algorithms -- On High Dimensional Search Space and Learning Methods -- Enhanced Over_Sampling Techniques for Imbalanced Big Data Set Classification -- Online Anomaly Detection in Big Data: The First Line of Defense Against Intruders -- Developing Modified Classifier for Big Data Paradigm: An Approach through Bio-Inspired Soft Computing -- Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing of Big Data -- An Efficient Approach for Mining High Utility Itemsets over Data Streams -- Event Detection in Location-Based Social Networks -- Part II. Applications -- Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey -- Big Data for Effective Management of Smart Grids -- Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics -- Predicting Spatiotemporal Impacts of Weather on Power Systems using Big Data Science -- Index.

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business. Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy. Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency) Computational intelligence with its underlying methodologies and tools helps address data analytics needs. The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

ISBN: 9783319534749

Standard No.: 10.1007/978-3-319-53474-9doiSubjects--Topical Terms:

595739
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Data science and big data = an environment of computational intelligence /
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245 0 0 $a Data science and big data $h [electronic resource] : $b an environment of computational intelligence / $c edited by Witold Pedrycz, Shyi-Ming Chen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a viii, 303 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.24
505 0 $a Part I. Fundamentals -- Large-Scale Clustering Algorithms -- On High Dimensional Search Space and Learning Methods -- Enhanced Over_Sampling Techniques for Imbalanced Big Data Set Classification -- Online Anomaly Detection in Big Data: The First Line of Defense Against Intruders -- Developing Modified Classifier for Big Data Paradigm: An Approach through Bio-Inspired Soft Computing -- Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing of Big Data -- An Efficient Approach for Mining High Utility Itemsets over Data Streams -- Event Detection in Location-Based Social Networks -- Part II. Applications -- Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey -- Big Data for Effective Management of Smart Grids -- Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics -- Predicting Spatiotemporal Impacts of Weather on Power Systems using Big Data Science -- Index.
520 $a This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business. Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy. Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency) Computational intelligence with its underlying methodologies and tools helps address data analytics needs. The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.
650 0 $a Computational intelligence. $3 595739
650 0 $a Big data. $3 2045508
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Big Data/Analytics. $3 2186785
650 2 4 $a Health Informatics. $3 892928
650 2 4 $a Health Care Management. $3 2054807
700 1 $a Pedrycz, Witold. $3 899642
700 1 $a Chen, Shyi-Ming. $3 1067501
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in big data ; $v v.24. $3 3227131
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-53474-9
950 $a Engineering (Springer-11647)