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Data Science and Big Data Computing

Frameworks and Methodologies

  • Book
  • © 2016

Overview

Editors:
  1. Zaigham Mahmood

    1. Department of Computing and Mathematics , University of Derby, Derby, United Kingdom

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  • Reviews the latest research and practice in data science and big data
  • Discusses tools and techniques for big data storage and analytics
  • Describes the frameworks relevant to data science, and their application

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Data Science Applications and Scenarios

    1. Front Matter

      Pages 1-1
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    2. An Interoperability Framework and Distributed Platform for Fast Data Applications

      • José Carlos Martins Delgado
      Pages 3-39

    3. Complex Event Processing Framework for Big Data Applications

      • Rentachintala Bhargavi
      Pages 41-56

    4. Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios

      • Anupam Biswas, Gourav Arora, Gaurav Tiwari, Srijan Khare, Vyankatesh Agrawal, Bhaskar Biswas
      Pages 57-78

    5. Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective

      • Ying Xie, Jing (Selena) He, Vijay V. Raghavan
      Pages 79-92

  3. Big Data Modelling and Frameworks

    1. Front Matter

      Pages 93-93
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    2. A Unified Approach to Data Modeling and Management in Big Data Era

      • Catalin Negru, Florin Pop, Mariana Mocanu, Valentin Cristea
      Pages 95-116

    3. Interfacing Physical and Cyber Worlds: A Big Data Perspective

      • Zartasha Baloch, Faisal Karim Shaikh, Mukhtiar A. Unar
      Pages 117-138

    4. Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data

      • Daniel Pop, Gabriel Iuhasz, Dana Petcu
      Pages 139-159

    5. An Analytics-Driven Approach to Identify Duplicate Bug Records in Large Data Repositories

      • Anjaneyulu Pasala, Sarbendu Guha, Gopichand Agnihotram, Satya Prateek B, Srinivas Padmanabhuni
      Pages 161-187

  4. Big Data Tools and Analytics

    1. Front Matter

      Pages 189-189
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    2. Large-Scale Data Analytics Tools: Apache Hive, Pig, and HBase

      • N. Maheswari, M. Sivagami
      Pages 191-220

    3. Big Data Analytics: Enabling Technologies and Tools

      • Mohanavadivu Periasamy, Pethuru Raj
      Pages 221-243

    4. A Framework for Data Mining and Knowledge Discovery in Cloud Computing

      • Derya Birant, Pelin Yıldırım
      Pages 245-267

    5. Feature Selection for Adaptive Decision Making in Big Data Analytics

      • Jaya Sil, Asit Kumar Das
      Pages 269-292

    6. Social Impact and Social Media Analysis Relating to Big Data

      • Nirmala Dorasamy, Nataša Pomazalová
      Pages 293-313

  5. Back Matter

    Pages 315-319
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Keywords

About this book

This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.

Reviews

“This title presents recent research and future trends in the area of big data. … It will be of value to students and researchers looking for research topics and to data scientists exploring ongoing work in the field of big data. Summing Up: Recommended. Graduate students; faculty and professionals.” (C. Tappert, Choice, Vol. 54 (7), March, 2017)

Editors and Affiliations

  • Department of Computing and Mathematics , University of Derby, Derby, United Kingdom

    Zaigham Mahmood

About the editor

Professor Zaigham Mahmood is a Senior Technology Consultant at Debesis Education UK and Associate Lecturer (Research) at the University of Derby, UK. He also holds positions as Foreign Professor at NUST and IIU in Islamabad, Pakistan, and Professor Extraordinaire at the North West University Potchefstroom, South Africa. Prof. Mahmood is a certified cloud computing instructor and a regular speaker at international conferences devoted to Cloud Computing and E-Government. His specialized areas of research include distributed computing, project management, and e-government. Among his many publications are the Springer titles Cloud Computing: Challenges, Limitations and R&D Solutions, Continued Rise of the Cloud, Cloud Computing: Methods and Practical Approaches, Software Engineering Frameworks for the Cloud Computing Paradigm, and Cloud Computing for Enterprise Architectures.

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