Journal
IETE TECHNICAL REVIEW
Volume 33, Issue 4, Pages 415-427Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02564602.2015.1117399
Keywords
Biomedical informatics; Multithreading; Biomedical ontologies; Ontology matching; Parallel processing; Parallel programming; Semantic web
Funding
- Microsoft Research Asia, Beijing, China
- MSIP (Ministry of Science, ICT&Future Planning), Korea, under IT/SW Creative research program [NIPA-2013-(H0503-131010)]
- Industrial Core Technology Development Program - Ministry of Trade, Industry and Energy (MOTIE, Korea) [10049079]
- National Research Foundation of Korea (NRF) grant - Korea government (MSIP) [NRF-2014R1A2A2A01003914]
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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 9.5times over a quad-core desktop PC and a four virtual machine (VM) cloud platform, respectively.
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