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)
List of Authors
End User License Agreement

7.4. Publish/Subscribe relevance

In top-k approaches, notified items are computed on the whole set of items, leading to delays of item delivery. In our approach, we consider the set of notified items for a given subscription, the so-called subscription history, and we use this history to filter out in real time the incoming item just after the matching process. This section presents our approach and the definitions adopted, and the instantiation is presented in section 7.5.

7.4.1. Items and histories

In our context, we define an item as a set of terms. Each term is associated with a term weight denoted by wi, which is used to compute distances and similarities. To compute novelty and diversity, a Pub/Sub system must keep already notified items, also called subscription history H. Each one is a time-ordered set of items linked to a subscription. Each time an item is notified for a subscription, it is added to its history.

7.4.2. Novelty

The objective when filtering by novelty is to...