Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Zhang, Wei Emma; Sheng, Quan Z.; Qin, Yongrui; Yao, Lina; Shemshadi, Ali; Taylor, Kerry
Publisher: ACM
Languages: English
Types: Part of book or chapter of book
Subjects: QA75

Classified by OpenAIRE into

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.\ud 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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware Query Suggestion by Mining Click-through and Session Data. In Proc. of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2008), pages 875{883, Las Vegas, Nevada, USA, August 2008.
    • [2] S. Dar, M. J. Franklin, B. T. Jonsson, D. Srivastava, and M. Tan. Semantic Data Caching and Replacement. In Proc. of the 22rd International Conference on Very Large Data Bases (VLDB1996), pages 330{341, Bombay, India, September 1996.
    • [3] E. S. Gardner. Exponential Smoothing: The State of The Art{Part II. International Journal of Forecasting, 22(4):637{666, 2006.
    • [4] R. Hasan. Predicting SPARQL Query Performance and Explaining Linked Data. In Proc. of the 11th Extended Semantic Web Conference (ESWC 2014), pages 795{805, Anissaras, Crete, Greece, May 2014.
    • [5] I. Jolli e. Principal component analysis. Wiley Online Library, 2005.
    • [6] L. Kaufman and P. Rousseeuw. Clustering by Means of Medoids. Dodge, Y. (ed.) Statistical Data Analysis based on the L1 Norm, 1987.
    • [7] J. Lehmann and L. Buhmann. AutoSPARQL: Let Users Query Your Knowledge Base. In Proc. of the 8th Extended Semantic Web Conference (ESWC 2011), pages 63{79, Heraklion, Crete, Greece, May 2011.
    • [8] J. Lorey and F. Naumann. Detecting SPARQL Query Templates for Data Prefetching. In Proc. of the 10th Extended Semantic Web Conference (ESWC 2013), pages 124{139, Montpellier, France, May 2013.
    • [9] M. Martin, J. Unbehauen, and S. Auer. Improving the Performance of Semantic Web Applications with SPARQL Query Caching. In Proc. of the 7th Extended Semantic Web Conference (ESWC 2010), pages 304{318, Heraklion, Crete, Greece, 2010.
    • [10] E. J. O'Neil, P. E. O'Neil, and G. Weikum. The LRU-K Page Replacement Algorithm For Database Disk Bu ering. In Proc. of the International Conference on Management of Data (SIGMOD 1993), pages 297{306, Washington, D.C., USA, May 1993.
    • [11] J. Perez, M. Arenas, and C. Gutierrez. Semantics and Complexity of SPARQL. ACM Transactions on Database Systems, 34(3), 2009.
    • [12] Q. Ren, M. H. Dunham, and V. Kumar. Semantic Caching and Query Processing. IEEE Transactions on Knowledge and Data Engineering, 15(1):192{210, 2003.
    • [13] A. Sanfeliu and K. Fu. A Distance Measure between Attributed Relational Graphs for Pattern Recognition. IEEE Transactions on Systems, Man, and Cybernetics, 13(3):353{362, 1983.
    • [14] R. Verborgh, O. Hartig, B. D. Meester, G. Haesendonck, L. D. Vocht, M. V. Sande, R. Cyganiak, P. Colpaert, E. Mannens, and R. V. de Walle. Querying Datasets on the Web with High Availability. In Proc. of the 13th International Semantic Web Conference (ISWC 2014), pages 180{196, Riva del Garda, Italy, October 2014.
    • [15] M. Yang and G. Wu. Caching Intermediate Result of SPARQL Queries. In Proc. of the 20th International World Wide Web Conference (WWW 2011), pages 159{160, Hyderabad, India, March 2011.
    • [16] W. E. Zhang, Q. Z. Sheng, K. Taylor, and Y. Qin. Identifying and Caching Hot Triples for E cient RDF Query Processing. In Proc. of the 20th International Conference on Database Systems for Advanced Applications (DASFAA 2015), pages 259{274, Hanoi, Vietnam, April 2015.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article