Data Science and Big Data Computing
Frameworks and Methodologies
Editors: Mahmood, Zaigham (Ed.)
Free Preview- 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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- About this book
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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.
- About the authors
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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.
- Reviews
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“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)
- Table of contents (13 chapters)
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An Interoperability Framework and Distributed Platform for Fast Data Applications
Pages 3-39
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Complex Event Processing Framework for Big Data Applications
Pages 41-56
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Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios
Pages 57-78
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Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective
Pages 79-92
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A Unified Approach to Data Modeling and Management in Big Data Era
Pages 95-116
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Table of contents (13 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Data Science and Big Data Computing
- Book Subtitle
- Frameworks and Methodologies
- Editors
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- Zaigham Mahmood
- Copyright
- 2016
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-31861-5
- DOI
- 10.1007/978-3-319-31861-5
- Hardcover ISBN
- 978-3-319-31859-2
- Softcover ISBN
- 978-3-319-81139-0
- Edition Number
- 1
- Number of Pages
- XXI, 319
- Number of Illustrations
- 68 b/w illustrations
- Topics
