Data Science and Big Data Computing Frameworks and Methodologies [Hardcover]

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.

Part I: Data Science Applications and Scenarios

An Interoperability Framework and Distributed Platform for Fast Data Applications
Jos? Carlos Martins Delgado

Complex Event Processing Framework for Big Data Applications
Renta Chintala Bhargavi

Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios
Anupam Biswas, Gourav Arora, Gaurav Tiwari, Srijan Khare, Vyankatesh Agrawal and Bhaskar Biswas

Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective
Ying Xie, Jing (Selena) He and Vijay V. Raghavan

Part II: Big Data Modelling and Frameworks

A Unified Approl³n