ENGLISH

Data Science and Big Data Computing: Frameworks and Methodologies

Book information

Publisher
Springer International Publishing : Imprint : Springer
Year
2016
ISBN
3319318594, 978-3-319-31859-2, 978-3-319-31861-5
Language
english
Format
PDF
Filesize
3 MB (3143640 bytes)
Edition
1st ed.
Pages
319\332
Library
kolxoz
Time added
2017-10-15 16:00:00

Description

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 Front Matter....Pages i-xxi Front Matter....Pages 1-1 An Interoperability Framework and Distributed Platform for Fast Data Applications....Pages 3-39 Complex Event Processing Framework for Big Data Applications....Pages 41-56 Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios....Pages 57-78 Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective....Pages 79-92 Front Matter....Pages 93-93 A Unified Approach to Data Modeling and Management in Big Data Era....Pages 95-116 Interfacing Physical and Cyber Worlds: A Big Data Perspective....Pages 117-138 Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data....Pages 139-159 An Analytics-Driven Approach to Identify Duplicate Bug Records in Large Data Repositories....Pages 161-187 Front Matter....Pages 189-189 Large-Scale Data Analytics Tools: Apache Hive, Pig, and HBase....Pages 191-220 Big Data Analytics: Enabling Technologies and Tools....Pages 221-243 A Framework for Data Mining and Knowledge Discovery in Cloud Computing....Pages 245-267 Feature Selection for Adaptive Decision Making in Big Data Analytics....Pages 269-292 Social Impact and Social Media Analysis Relating to Big Data....Pages 293-313 Back Matter....Pages 315-319

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