Using Hadoop to implement a semantic method of assessing the quality of research medical datasets

Stephen Bonner; Ibad Kureshi; Grigoris Antoniou; David Corsair; Laura Moss; Illias Tachmazidis

doi:10.1145/2640087.2644163

Using Hadoop to implement a semantic method of assessing the quality of research medical datasets

Stephen Bonner, Ibad Kureshi, Grigoris Antoniou, David Corsair, Laura Moss, Illias Tachmazidis

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

Abstract

In this paper a system for storing and querying medical RDF data using Hadoop is developed. This approach enables us to create an inherently parallel framework that will scale the workload across a cluster. Unlike existing solutions, our framework uses highly optimised joining strategies to enable the completion of eight separate SPAQL queries, comprised of over eighty distinct joins, in only two Map/Reduce iterations. Results are presented comparing an optimised version of our solution against Jena TDB, demonstrating the superior performance of our system and its viability for assessing the quality of medical data.

Original language	English
Title of host publication	Proceedings of the 3rd ASE International Conference on Big Data Science and Computing, BIGDATASCIENCE 2014
Publisher	Association for Computing Machinery (ACM)
Volume	04-07-August-2014
ISBN (Electronic)	9781450328913
DOIs	https://doi.org/10.1145/2640087.2644163
Publication status	Published - 4 Aug 2014
Event	3rd ASE International Conference on Big Data Science and Computing - Beijing, China Duration: 4 Aug 2014 → 7 Aug 2014 Conference number: 3 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=36881&copyownerid=62784 (Link to Conference Website)

Conference

Conference	3rd ASE International Conference on Big Data Science and Computing
Abbreviated title	BigDataScience2014
Country/Territory	China
City	Beijing
Period	4/08/14 → 7/08/14
Internet address	http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=36881&copyownerid=62784 (Link to Conference Website)

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10.1145/2640087.2644163Licence: Other

Cite this

Bonner, S., Kureshi, I., Antoniou, G., Corsair, D., Moss, L., & Tachmazidis, I. (2014). Using Hadoop to implement a semantic method of assessing the quality of research medical datasets. In Proceedings of the 3rd ASE International Conference on Big Data Science and Computing, BIGDATASCIENCE 2014 (Vol. 04-07-August-2014). [2644163] Association for Computing Machinery (ACM). https://doi.org/10.1145/2640087.2644163

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Bonner, S, Kureshi, I, Antoniou, G, Corsair, D, Moss, L & Tachmazidis, I 2014, Using Hadoop to implement a semantic method of assessing the quality of research medical datasets. in Proceedings of the 3rd ASE International Conference on Big Data Science and Computing, BIGDATASCIENCE 2014. vol. 04-07-August-2014, 2644163, Association for Computing Machinery (ACM), 3rd ASE International Conference on Big Data Science and Computing, Beijing, China, 4/08/14. https://doi.org/10.1145/2640087.2644163

Using Hadoop to implement a semantic method of assessing the quality of research medical datasets. / Bonner, Stephen; Kureshi, Ibad; Antoniou, Grigoris et al.

Proceedings of the 3rd ASE International Conference on Big Data Science and Computing, BIGDATASCIENCE 2014. Vol. 04-07-August-2014 Association for Computing Machinery (ACM), 2014. 2644163.

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

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AU - Kureshi, Ibad

AU - Antoniou, Grigoris

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AU - Moss, Laura

AU - Tachmazidis, Illias

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BT - Proceedings of the 3rd ASE International Conference on Big Data Science and Computing, BIGDATASCIENCE 2014

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Bonner S, Kureshi I, Antoniou G, Corsair D, Moss L, Tachmazidis I. Using Hadoop to implement a semantic method of assessing the quality of research medical datasets. In Proceedings of the 3rd ASE International Conference on Big Data Science and Computing, BIGDATASCIENCE 2014. Vol. 04-07-August-2014. Association for Computing Machinery (ACM). 2014. 2644163 doi: 10.1145/2640087.2644163

Using Hadoop to implement a semantic method of assessing the quality of research medical datasets

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