research-article

Efficient processing of SPARQL queries over GraphFrames

Authors:

Ramazan Ali Bahrami,

Muhammad AbulaishAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 678 - 685

https://doi.org/10.1145/3106426.3106534

Published: 23 August 2017 Publication History

Abstract

With the advent of huge data management systems storing voluminous data, there arises a need to develop efficient data analytics techniques for knowledge discovery at different levels of granularity. Resource Description Framework (RDF), mainly developed for Semantic Web, is presumably a good option when considering graph databases dealing with huge real-world data. RDF models information in the form of triples <subject, predicate, object>, and is considered as a useful tool to store graph data (aka linked data) where each edge can be stored as a triple. Due to existence of huge amount of linked data, mostly in the form of graphs, graph mining has been successful in attracting researchers from different research fields for efficient handling (storage, indexing, retrieval, etc.) of graph data. As a result, various APIs like GraphX and GraphFrames are developed to facilitate relational queries over graph data. Though GraphX is older than GraphFrames and processing SPARQL queries over GraphX has been explored by some researchers, to the best of our knowledge, SPARQL query processing over GraphFrames has not been explored yet. In this paper, we present an initial study on query-specific search space pruning and query optimization approach to process SPARQL queries over GraphFrames in an efficient manner. The experimental results, in terms of low response time for query execution, are encouraging, and give way to invest more research efforts in this direction.

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Cited By

Troullinou GAgathangelos GKondylakis HStefanidis KPlexousakis D(2024)DIAERESIS: RDF data partitioning and query processing on SPARKSemantic Web10.3233/SW-243554(1-27)Online publication date: 6-Mar-2024
https://doi.org/10.3233/SW-243554
Moertini VAdithia M(2021)Uncovering Active Communities from Directed Graphs on Distributed Spark Frameworks, Case Study: Twitter DataBig Data and Cognitive Computing10.3390/bdcc50400465:4(46)Online publication date: 22-Sep-2021
https://doi.org/10.3390/bdcc5040046
Guo JWang Y(2021)Summarizing RDF graphs using Node Importance and Query History2021 International Conference on Service Science (ICSS)10.1109/ICSS53362.2021.00016(51-58)Online publication date: May-2021
https://doi.org/10.1109/ICSS53362.2021.00016
Show More Cited By

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cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

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Published: 23 August 2017

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WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Troullinou GAgathangelos GKondylakis HStefanidis KPlexousakis D(2024)DIAERESIS: RDF data partitioning and query processing on SPARKSemantic Web10.3233/SW-243554(1-27)Online publication date: 6-Mar-2024
https://doi.org/10.3233/SW-243554
Moertini VAdithia M(2021)Uncovering Active Communities from Directed Graphs on Distributed Spark Frameworks, Case Study: Twitter DataBig Data and Cognitive Computing10.3390/bdcc50400465:4(46)Online publication date: 22-Sep-2021
https://doi.org/10.3390/bdcc5040046
Guo JWang Y(2021)Summarizing RDF graphs using Node Importance and Query History2021 International Conference on Service Science (ICSS)10.1109/ICSS53362.2021.00016(51-58)Online publication date: May-2021
https://doi.org/10.1109/ICSS53362.2021.00016
Agathangelos GTroullinou GKondylakis HStefanidis KPlexousakis D(2018)RDF Query Answering Using Apache Spark: Review and Assessment2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2018.00016(54-59)Online publication date: Apr-2018
https://doi.org/10.1109/ICDEW.2018.00016

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