Big data analytics

Big data analytics

by Baeza-Yates, R., (Ricardo) Govindaraju, Venugopal Raghavan, Vijay Rao, C. R.

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While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networ…

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Title Statements

  • Full title :Big data analytics / edited by Venu Govindaraju, Vijay Raghavan, C. R. Rao ; contributors Ricardo Baeza-Yates [and thirty three others].

Summary

While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massiv.

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Edition Statements

  • Publication Date:©2015

Publication Statements

  • Publisher(s):North-Holland
  • Place of Publication:Amsterdam, [Netherlands]
  • Language:eng
  • Publication Date:©2015

Notes

Description based upon print version of record.

The Physical Object

  • Description:1 online resource (391 p.).

Edition Identifiers

  • ISBN:0444634975, 9780444634979, 0444634924, 9780444634924
  • oclc:on1162077470
  • LC Classification:HD30.215
  • Dewey Decimal:621.310285/57

Table of Contents

""Front Cover""; ""Big Data Analytics""; ""Copyright""; ""Contents""; ""Contributors""; ""Preface""; ""Part A: Modeling and Analytics""; ""Chapter 1: Document Informatics for Scientific Learning and Accelerated Discovery""; ""1. Introduction""; ""1.1 Sample Use Case""; ""1.1.1 Description""; ""1.1.2 Current Research Process""; ""1.1.3 Problems with the Current Process""; ""1.1.4 The Future Process""; ""1.1.5 Benefits of the Future Process""; ""2. How Document Informatics Will Aid Materials Discovery""; ""2.1 Motivation""; ""2.2 Big Data Justification""

""2.3 Challenges of Meta-Learning in Materials Research""""3. The General Research Framework""; ""4. Pilot Implementation""; ""4.1 Objective 1: To Design and Develop a Time-Based, Hierarchical Topic Model""; ""4.1.1 Problem Description""; ""4.1.2 Prior Work""; ""4.1.3 Research Contributions""; ""4.2 Objective 2: To Implement Algorithms for Extracting Text from x�y Plots and Tables""; ""4.2.1 Problem Description""; ""4.2.2 Prior Work""; ""4.2.3 Research Contributions""; ""4.3 Objective 3: To Develop an Interactive, Materials Network Visualization Tool""; ""4.3.1 Problem Description""

""4.3.2 Prior Work""""4.3.3 Research Contribut…

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Subjects

Big data -- Statistical methods Données volumineuses -- Méthodes statis…

Writers

Baeza-Yates, R., (Ricardo) Govindaraju, Venugopal Raghavan, Vijay Rao, C. R.

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