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Federated query processing on linked data: a qualitative survey and open challenges

Published online by Cambridge University Press:  30 October 2015

Damla Oguz ,
Belgin Ergenc ,
Shaoyi Yin ,
Oguz Dikenelli  and
Abdelkader Hameurlain
Damla Oguz
Affiliation:
Department of Computer Engineering, Izmir Institute of Technology, 35430 Izmir, Turkey e-mail: damlaoguz@iyte.edu.tr, belginergenc@iyte.edu.tr Department of Computer Engineering, Ege University, 35100 Izmir, Turkey e-mail: oguz.dikenelli@ege.edu.tr IRIT Laboratory, Paul Sabatier University, 31062 Toulouse, France e-mail: shaoyi.yin@irit.fr, abdelkader.hameurlain@irit.fr
Belgin Ergenc
Affiliation:
Department of Computer Engineering, Izmir Institute of Technology, 35430 Izmir, Turkey e-mail: damlaoguz@iyte.edu.tr, belginergenc@iyte.edu.tr
Shaoyi Yin
Affiliation:
IRIT Laboratory, Paul Sabatier University, 31062 Toulouse, France e-mail: shaoyi.yin@irit.fr, abdelkader.hameurlain@irit.fr
Oguz Dikenelli
Affiliation:
Department of Computer Engineering, Ege University, 35100 Izmir, Turkey e-mail: oguz.dikenelli@ege.edu.tr
Abdelkader Hameurlain
Affiliation:
IRIT Laboratory, Paul Sabatier University, 31062 Toulouse, France e-mail: shaoyi.yin@irit.fr, abdelkader.hameurlain@irit.fr
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Abstract

A large number of data providers publish and connect their structured data on the Web as linked data. Thus, the Web of data becomes a global data space. In this paper, we initially give an overview of query processing approaches used in this interlinked and distributed environment, and then focus on federated query processing on linked data. We provide a detailed and clear insight on data source selection, join methods and query optimization methods of existing query federation engines. Furthermore, we present a qualitative comparison of these engines and give a complementary comparison of the measured metrics of each engine with the idea of pointing out the major strengths of each one. Finally, we discuss the major challenges of federated query processing on linked data.

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Type
Articles
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The Knowledge Engineering Review , Volume 30 , Issue 5 , November 2015 , pp. 545 - 563
Copyright
© Cambridge University Press, 2015 

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