SECF: Improving SPARQL Querying performance with proactive fetching and Caching

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

doi:10.1145/2851613.2851846

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 proceeding › Conference contribution › peer-review

8 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 language	English
Title of host publication	2016 Symposium on Applied Computing, SAC 2016
Publisher	Association for Computing Machinery (ACM)
Pages	362-367
Number of pages	6
Volume	04-08-April-2016
ISBN (Electronic)	9781450337397
DOIs	https://doi.org/10.1145/2851613.2851846
Publication status	Published - 4 Apr 2016
Externally published	Yes
Event	31st Annual Association for Computing Machinery Symposium on Applied Computing - Pisa, Italy Duration: 4 Apr 2016 → 8 Apr 2016 Conference number: 31 https://www.sigapp.org/sac/sac2016/ (Link to Symposium Website )

Conference

Conference	31st Annual Association for Computing Machinery Symposium on Applied Computing
Abbreviated title	SAC 2016
Country/Territory	Italy
City	Pisa
Period	4/04/16 → 8/04/16
Internet address	https://www.sigapp.org/sac/sac2016/ (Link to Symposium Website )

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10.1145/2851613.2851846Licence: Other

http://eprints.hud.ac.uk/id/eprint/29761/Licence: Unspecified

Cite this

@inproceedings{1e110783366e42939ca49b87b8eed85b,

title = "SECF: Improving SPARQL Querying performance with proactive fetching and Caching",

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.",

keywords = "Caching, Mses, Query suggestion, SPARQL",

author = "Zhang, {Wei Emma} and Sheng, {Quan Z.} and Yongrui Qin and Lina Yao and Ali Shemshadi and Kerry Taylor",

year = "2016",

month = apr,

day = "4",

doi = "10.1145/2851613.2851846",

language = "English",

volume = "04-08-April-2016",

pages = "362--367",

booktitle = "2016 Symposium on Applied Computing, SAC 2016",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "31st Annual Association for Computing Machinery Symposium on Applied Computing, SAC 2016 ; Conference date: 04-04-2016 Through 08-04-2016",

url = "https://www.sigapp.org/sac/sac2016/",

}

Zhang, WE, Sheng, QZ, 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, Association for Computing Machinery (ACM), pp. 362-367, 31st Annual Association for Computing Machinery Symposium on Applied Computing, Pisa, Italy, 4/04/16. https://doi.org/10.1145/2851613.2851846

SECF : Improving SPARQL Querying performance with proactive fetching and Caching. / Zhang, Wei Emma; Sheng, Quan Z.; Qin, Yongrui et al.

2016 Symposium on Applied Computing, SAC 2016. Vol. 04-08-April-2016 Association for Computing Machinery (ACM), 2016. p. 362-367.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SECF

T2 - 31st Annual Association for Computing Machinery Symposium on Applied Computing

AU - Zhang, Wei Emma

AU - Sheng, Quan Z.

AU - Qin, Yongrui

AU - Yao, Lina

AU - Shemshadi, Ali

AU - Taylor, Kerry

N1 - Conference code: 31

PY - 2016/4/4

Y1 - 2016/4/4

N2 - 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.

AB - 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.

KW - Caching

KW - Mses

KW - Query suggestion

KW - SPARQL

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U2 - 10.1145/2851613.2851846

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M3 - Conference contribution

AN - SCOPUS:84975797452

VL - 04-08-April-2016

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EP - 367

BT - 2016 Symposium on Applied Computing, SAC 2016

PB - Association for Computing Machinery (ACM)

Y2 - 4 April 2016 through 8 April 2016

ER -

SECF: Improving SPARQL Querying performance with proactive fetching and Caching

Abstract

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