Data Science and Big Data Computing

Frameworks and Methodologies

  • Zaigham Mahmood

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Data Science Applications and Scenarios

    1. Front Matter
      Pages 1-1
    2. Anupam Biswas, Gourav Arora, Gaurav Tiwari, Srijan Khare, Vyankatesh Agrawal, Bhaskar Biswas
      Pages 57-78
  3. Big Data Modelling and Frameworks

    1. Front Matter
      Pages 93-93
    2. Catalin Negru, Florin Pop, Mariana Mocanu, Valentin Cristea
      Pages 95-116
    3. Zartasha Baloch, Faisal Karim Shaikh, Mukhtiar A. Unar
      Pages 117-138
    4. Daniel Pop, Gabriel Iuhasz, Dana Petcu
      Pages 139-159
    5. 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
    2. N. Maheswari, M. Sivagami
      Pages 191-220
    3. Mohanavadivu Periasamy, Pethuru Raj
      Pages 221-243
    4. Derya Birant, Pelin Yıldırım
      Pages 245-267
    5. Nirmala Dorasamy, Nataša Pomazalová
      Pages 293-313
  5. Back Matter
    Pages 315-319

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 an authoritative collection of thirty-six 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.

Topics and features:

  • Reviews a framework for fast data applications, a technique for complex event processing, and a selection of agglomerative approaches for partitioning of networks
  • Discusses a big data approach to identifying minimum-sized influential vertices from large-scale weighted graphs
  • Introduces a unified approach to data modeling and management, and offers a distributed computing perspective on interfacing physical and cyber worlds
  • Presents techniques for machine learning in the context of big data, and describes an analytics-driven approach to identifying duplicate records in large data repositories
  • Examines various enabling technologies and tools for data mining, including Apache Hadoop
  • Proposes a novel framework for data extraction and knowledge discovery, and provides case studies on adaptive decision making and social media analysis

This comprehensive volume is a valuable reference for researchers, lecturers and students interested in data science and big data, in addition to professionals seeking to adopt the latest approaches in data analytics to gain business intelligence for strategic decision-making.


Big Data Modeling and Management Data Mining and Predictive Analytics Security, Privacy, Safety and Backup Segmentation, Storage and Retrieval Social Impact and Social Media Analysis

Editors and affiliations

  • Zaigham Mahmood
    • 1
  1. 1.Department of Computing and Mathematics University of DerbyDerbyUnited Kingdom

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