Effective Big Data Management and Opportunities for Implementation

Effective Big Data Management and Opportunities for Implementation

Manoj Kumar Singh (Adama Science and Technology University, Ethiopia) and Dileep Kumar G. (Adama Science and Technology University, Ethiopia)
Indexed In: SCOPUS
Release Date: June, 2016|Copyright: © 2016 |Pages: 324
ISBN13: 9781522501824|ISBN10: 1522501827|EISBN13: 9781522501831|DOI: 10.4018/978-1-5225-0182-4

Description

“Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless.

Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Applied Big Data
  • Complex Data
  • Data Analytics
  • Data Management
  • Data visualization
  • Information Retrieval
  • Large-Scale Recommendation Systems
  • Open Data Sets

Reviews and Testimonials

Editors Singh and G. present readers with a collection of academic articles and scholarly contributions focused on the management of big data and opportunities for implementing best practices in big data management contexts. The fifteen selections that make up the main body of the text are devoted to big data mining, the challenges of data cleansing with data warehouses, the big data analysis pipeline and its technical challenges, and many other related subjects.

– ProtoView Reviews

[...]. The book is made up of 15 chapters with titles such as “Big Data: Challenges, Opportunities, and Realities,” “Big Data Analysis,” and “Using Big Data to Improve the Educational Infrastructure and Learning Paradigm.” Each of the chapters contains an abstract, significant discussion, helpful tables and figures for illustrative purposes, a conclusion, and a reference list. Each entry also contains a key terms and definitions list, which is useful for the reader to consult if confusion arises over the meaning or origin of a word in the context of the article.
Chapter 11, “Using Big Data to Improve the Educational Infrastructure and Learning Paradigm” discusses the advances in learning technology as they relate to big data. As education is deliverable in new and varied ways, more data is generated from the delivery of that education. As such, more data is generated by users who receive education from a distance. Data generated from the education sector can be broken down into four categories: descriptive data, behavior data, interaction data, and attitudinal data. Out of these four categories, myriad possibilities present themselves. Of course, there are limitations to this tremendously large amount of data—namely, the ability for student assessment. The authors caution away from instructors who are too “data driven” at the expense of cultivating and embracing real connections with their students. The macro trends gathered from data should not replace the micro trends, but can provide a useful roadmap on a path forward for future growth.
Overall, the book is a useful resource for understanding the role that big data plays in our society. While sometimes the reading is dense that can likely be attributed to the nature of the material rather than the fault of the author. This book is strongly recommended for researchers and students alike as they begin to grapple with the implications of a data-driven world. This book is recommended for academic libraries.

– Sara Mofford, ARBA Reviews

Table of Contents and List of Contributors

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Indices