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SECF: Improving SPARQL Querying Performance with Proactive Fetching and Caching

Zhang, Wei Emma, Sheng, Quan Z., Qin, Yongrui, Yao, Lina, Shemshadi, Ali and Taylor, Kerry (2016) SECF: Improving SPARQL Querying Performance with Proactive Fetching and Caching. In: The 31st ACM Symposium on Applied Computing (SAC), April 4-8, 2016, Pisa, Italy.

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Abstract

Querying on SPARQL endpoints may be unsatisfactory due to high latency of connections to the endpoints. Caching is an important way to accelerate the query response speed. In this paper, we propose SPARQL Endpoint Caching Framework (SECF), a client-side caching framework for this purpose. In particular, we prefetch and cache the results of similar queries to recently cached query aiming to improve the overall querying performance. The similarity between queries are calculated via an improved Graph Edit Distance (GED) function. We also adapt a smoothing method to implement the cache replacement. The empirical evaluations on real world queries show that our approach has great potential to enhance the cache hit rate and accelerate the querying speed on SPARQL endpoints.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Yongrui Qin
Date Deposited: 08 Nov 2016 09:44
Last Modified: 21 Jul 2017 09:43
URI: http://eprints.hud.ac.uk/id/eprint/29761

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