Physical knowledge graph design for efficient federated query processing

Arenas Guerrero, Julián (2020). Physical knowledge graph design for efficient federated query processing. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Physical knowledge graph design for efficient federated query processing
Author/s:
  • Arenas Guerrero, Julián
Contributor/s:
  • Corcho García, Óscar
  • Chaves Fraga, David
Item Type: Thesis (Master thesis)
Masters title: Inteligencia Artificial
Date: July 2020
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In the current context of data prevalence, knowledge graphs have emerged as a popular method to integrate and extract value of heterogeneous large-scale data sources. In this thesis, we propose a novel idea for efficiently computing queries in a knowledge graph: the optimization of its physical design by combining materialization and virtualization. To this end, we carried out an empirical comparison between these two approaches and studied the parameters that have an impact on their performances. State of the art benchmarks and engines are used throughout the work. We found that, for a lower number of triples, the efficiency is enhanced in the case of materialization. In contrast, virtualization tends to improve as the data size increases. The viability of our approach is confirmed, proving that this hybrid approach can indeed enhance considerably query performance by materializing specific parts of knowledge graphs. It is, to our knowledge, the first time that this idea has been tested, and we believe that this work can be the starting point for future research focusing in query performance.

More information

Item ID: 63647
DC Identifier: http://oa.upm.es/63647/
OAI Identifier: oai:oa.upm.es:63647
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 08 Sep 2020 08:17
Last Modified: 08 Sep 2020 08:17
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