Enterprise Big Data Engineering, Analytics, and Management

  • 6h 22m
  • Martin Atzmueller, Samia Oussena, Thomas Roth-Berghofer (eds)
  • IGI Global
  • 2016

The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary.

Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field.

About the Editors

Martin Atzmueller is adjunct professor (Privatdozent) at the University of Kassel and heads the Ubiquitous Data Mining Team at the Research Center for Information System Design (ITeG), Hertie Chair for Knowledge and Data Engineering. His research areas include data mining, ubiquitous social computing, web and network science, machine learning, and Big Data. He earned his habilitation (Dr. habil.) in 2013 at the University of Kassel, and received his Ph.D. in Computer Science from the University of Wuerzburg in 2006. He studied Computer Science at the University of Texas at Austin (USA) and at the University of Wuerzburg where he completed his MSc in Computer Science.

Samia Oussena is a reader at University of West London and has a research background in methodologies and software application development. Prior to academia, she gained an extensive industrial experience in software development. She has led and been involved in a number of application development projects for the Insurance and oil and gas sector. More recently, her research interests are in developing software methods to support the development of enterprise applications/systems. Of particular interest is the use of model driven practice to the development of smart enterprise systems.

Thomas Roth-Berghofer's research focuses on aspects of smarter communication with personalised computing systems. He specialises in experience reuse using case-based reasoning and explanation-aware computing. He is Professor of Artificial Intelligence and Head of Research and Enterprise of the School of Computing and Engineering at the University of West London. Dr Roth-Berghofer obtained his PhD from the University of Kaiserslautern, Germany, while working as software developer, technical consultant, and department head of quality management and customer support. With industry experience under his belt he further pursued his career in academia at the University of Heidelberg and the German Research Centre for Artificial Intelligence DFKI GmbH as well as the University of Hildesheim before he joined the University of West London in 2011. Dr Roth-Berghofer has more than 100 refereed publications. He organised many workshops and conferences on such topics as case-based reasoning, context, explanation, and knowledge management.

In this Book

  • Foreword
  • How Big Does Big Data Need to Be?
  • Strategic Management of Data and Challenges for Organizations—Strategy Development and Business Value
  • Data Stream Mining of Event and Complex Event Streams—A Survey of Existing and Future Technologies and Applications in Big Data
  • Research Challenges in Big Data Analytics
  • Descriptive and Predictive Analytical Methods for Big Data
  • A Framework to Analyze Enterprise Social Network Data
  • Big Data Analytics Using Local Exceptionality Detection
  • Statistical Features for Extractive Automatic Text Summarization
  • Data Modeling and Knowledge Discovery in Process Industries
  • Data Preparation for Big Data Analytics—Methods and Experiences
  • Semantification of Large Corpora of Technical Documentation
  • Application of Complex Event Processing Techniques to Big Data Related to Healthcare—A Systematic Literature Review of Case Studies
  • Using Big Data in Collaborative Learning
  • Compilation of References