Book Image

NoSQL Data Models

By : Olivier Pivert
Book Image

NoSQL Data Models

By: Olivier Pivert

Overview of this book

Big Data environments are now to be handled in most current applications, this book addresses the latest issues and hurdles that are encountered in such environments. The book begins by presenting an overview of NoSQL languages and systems. Then, you’ll evaluate SPARQL queries over large RDF datasets and devise a solution that will use the MapReduce framework to process SPARQL graph patterns. Next, you’ll handle the production of web data, generate a set of links between two different datasets and overcome different heterogeneity problems. Moving ahead, you’ll take the multi-graph based approach to overcome challenges faced by the RDF data management community. Finally, you’ll deal with the flexible querying of graph databases and textual data management. By the end of this book, you’ll have gathered essential information on big data challenges faced by NoSQL databases.
Table of Contents (11 chapters)
Preface
8
List of Authors
9
Index
10
End User License Agreement

4.1. Introduction

Over the past years, two important concepts have emerged in the computing world: Big Data and Cloud Computing. According to the NIST Big Data Public Working Group1, Big Data is data that exceed the capacity or capability of current or conventional methods and systems. In [ZIK 11], IBM defines the term Big Data as information that cannot be processed or analyzed using traditional processes or tools. It is mainly based on the three-Vs model, where the three Vs refer to the volume, velocity and variety properties. We define these properties as follows:

  • Volume: this denotes the processing of large amounts of information. Over the past decades, several high technologies have appeared and they accompany people in their everyday life. If they can track and record something, they typically do it. For instance, simple actions (e.g. taking your smartphone out of your pocket, checking in for a plane, scanning your badge into work, buying a song on iTunes, etc.) generate...