Evaluating Large-Scale Biomedical Ontology Matching Over Parallel Platforms

Biomedical systems have been using ontology matching as a primary technique for heterogeneity resolution. However, the natural intricacy and vastness of biomedical data have compelled biomedical ontologies to become large-scale and complex; consequently, biomedical ontology matching has become a com...

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Veröffentlicht in:Technical review - IETE Jg. 33; H. 4; S. 415 - 427
Hauptverfasser: Amin, Muhammad Bilal, Khan, Wajahat Ali, Hussain, Shujaat, Bui, Dinh-Mao, Banos, Oresti, Kang, Byeong Ho, Lee, Sungyoung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 03.07.2016
Schlagworte:
Biomedical informatics
Biomedical ontologies
Multithreading
Ontology matching
Parallel processing
Parallel programming
Semantic web
ISSN:0256-4602, 0974-5971
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Zusammenfassung:Biomedical systems have been using ontology matching as a primary technique for heterogeneity resolution. However, the natural intricacy and vastness of biomedical data have compelled biomedical ontologies to become large-scale and complex; consequently, biomedical ontology matching has become a computationally intensive task. Our parallel heterogeneity resolution system, i.e., SPHeRe, is built to cater the performance needs of ontology matching by exploiting the parallelism-enabled multicore nature of today's desktop PC and cloud infrastructure. In this paper, we present the execution and evaluation results of SPHeRe over large-scale biomedical ontologies. We evaluate our system by integrating it with the interoperability engine of a clinical decision support system (CDSS), which generates matching requests for large-scale NCI, FMA, and SNOMED-CT biomedical ontologies. Results demonstrate that our methodology provides an impressive performance speedup of 4.8 and 9.5 times over a quad-core desktop PC and a four virtual machine (VM) cloud platform, respectively.
ISSN:0256-4602
0974-5971
DOI:10.1080/02564602.2015.1117399