Efficient query evaluation techniques over large amount of distributed linked data

TL;DR

This paper introduces distributed algorithms for efficient querying of large linked data sets, utilizing MapReduce and query decomposition techniques to improve performance and scalability.

Contribution

It presents a family of data-agnostic distributed query evaluation algorithms and explores query decomposition methods to enhance distributed linked data querying.

Findings

Algorithms are independent of storage and implementation details.

Distributed implementation using MapReduce shows promising performance.

Query decomposition improves query answering efficiency.

Abstract

As RDF becomes more widely established and the amount of linked data is rapidly increasing, the efficient querying of large amount of data becomes a significant challenge. In this paper, we propose a family of algorithms for querying large amount of linked data in a distributed manner. These query evaluation algorithms are independent of the way the data is stored, as well as of the particular implementation of the query evaluation. We then use the MapReduce paradigm to present a distributed implementation of these algorithms and experimentally evaluate them, although the algorithms could be straightforwardly translated into other distributed processing frameworks. We also investigate and propose multiple query decomposition approaches of Basic Graph Patterns (subclass of SPARQL queries) that are used to improve the overall performance of the distributed query answering. A deep analysis of the effectiveness of these decomposition algorithms is also provided.