SECF: Improving SPARQL Querying performance with proactive fetching and Caching

Wei Emma Zhang, Quan Z. Sheng, Yongrui Qin, Lina Yao, Ali Shemshadi, Kerry Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2016 Symposium on Applied Computing, SAC 2016
PublisherAssociation for Computing Machinery (ACM)
Pages362-367
Number of pages6
Volume04-08-April-2016
ISBN (Electronic)9781450337397
DOIs
Publication statusPublished - 4 Apr 2016
Externally publishedYes
Event31st Annual Association for Computing Machinery Symposium on Applied Computing - Pisa, Italy
Duration: 4 Apr 20168 Apr 2016
Conference number: 31
https://www.sigapp.org/sac/sac2016/ (Link to Symposium Website )

Conference

Conference31st Annual Association for Computing Machinery Symposium on Applied Computing
Abbreviated titleSAC 2016
CountryItaly
CityPisa
Period4/04/168/04/16
Internet address

Cite this

Zhang, W. E., Sheng, Q. Z., Qin, Y., Yao, L., Shemshadi, A., & Taylor, K. (2016). SECF: Improving SPARQL Querying performance with proactive fetching and Caching. In 2016 Symposium on Applied Computing, SAC 2016 (Vol. 04-08-April-2016, pp. 362-367). Association for Computing Machinery (ACM). https://doi.org/10.1145/2851613.2851846