Data Science and Big Data Computing

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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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.

• 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

“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)

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

  Zaigham Mahmood

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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