Data Science and Big Data Computing

Frameworks and Methodologies
Softcover Nachdruck of the original 1. Auflage 2016
ISBN: 978-3-319-81139-0
Verlag: Springer International Publishing

Buch, Englisch, 319 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 5212 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Mahmood, Zaigham

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

Part I: Data Science Applications and Scenarios

An Interoperability Framework and Distributed Platform for Fast Data Applications
José 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 Approach to Data Modelling and Management in Big Data Era
Catalin Negru, Florin Pop, Mariana Mocanu and Valentin Cristea

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

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

An Analytics Driven Approach to Identify Duplicate Bug Records in Large Data Repositories
Anjaneyulu 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 HBase
N. Maheswari and M. Sivagami

Big Data Analytics: Enabling Technologies and Tools
Mohanavadivu Periasamy and Pethuru Raj

A Framework for Data Mining and Knowledge Discovery in Cloud Computing
Derya Birant and Pelin Yildirim

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

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

Über Autor(innen)

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.