Data Science and Big Data Computing (eBook)

Frameworks and Methodologies

Zaigham Mahmood (Herausgeber)

eBook Download: PDF

2016 | 1st ed. 2016
XXI, 319 Seiten
Springer International Publishing (Verlag)
978-3-319-31861-5 (ISBN)

Infos

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.

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.

Inhalt

Part I: Data Science Applications and Scenarios

An Interoperability Framework and Distributed Platform for Fast Data ApplicationsJosé Carlos Martins Delgado

Complex Event Processing Framework for Big Data ApplicationsRenta Chintala Bhargavi

Agglomerative Approaches for Partitioning of Networks in Big Data ScenariosAnupam 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 PerspectiveYing Xie, Jing (Selena) He and Vijay V. Raghavan

Part II: Big Data Modelling and Frameworks

A Unified Approach to Data Modelling and Management in Big Data EraCatalin Negru, Florin Pop, Mariana Mocanu and Valentin Cristea

Interfacing Physical and Cyber Worlds: A Big Data PerspectiveZartasha Baloch, Faisal Karim Shaikh and Mukhtiar A. Unar

Distributed Platforms and Cloud Services: Enabling Machine Learning for Big DataDaniel Pop, Gabriel Iuhasz and Dana Petcu

An Analytics Driven Approach to Identify Duplicate Bug Records in Large Data RepositoriesAnjaneyulu Pasala, Sarbendu Guha, Gopichand Agnihotram, Satya Prateek B and Srinivas Padmanabhuni

Part III: Big Data Tools and Analytics

Large Scale Data Analytics Tools: Apache Hive, Pig and HBaseN. Maheswari and M. Sivagami

Big Data Analytics: Enabling Technologies and ToolsMohanavadivu Periasamy and Pethuru Raj

A Framework for Data Mining and Knowledge Discovery in Cloud ComputingDerya Birant and Pelin Yıldırım

Feature Selection for Adaptive Decision Making in Big Data AnalyticsJaya Sil and Asit Kumar Das

Social Impact and Social Media Analysis Relating to Big DataNirmala Dorasamy and Nataša Pomazalová

Produkt-Details

• Erscheint lt. Verlag: 5.7.2016
• Zusatzinfo: XXI, 319 p. 68 illus.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Schlagworte: 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
• ISBN-10: 3-319-31861-6 / 3319318616
• ISBN-13: 978-3-319-31861-5 / 9783319318615