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Article

A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing

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Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia
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Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia
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Computer Science Department Effat, College of Engineering, Effat University, P.O. Box 34689, Jeddah 22332, Saudi Arabia
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Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(20), 7092; https://doi.org/10.3390/app10207092
Received: 11 September 2020 / Revised: 30 September 2020 / Accepted: 6 October 2020 / Published: 12 October 2020
(This article belongs to the Special Issue Data Science for Healthcare Intelligence)
Massive heterogeneous big data residing at different sites with various types and formats need to be integrated into a single unified view before starting data mining processes. Furthermore, in most of applications and research, a single big data source is not enough to complete the analysis and achieve goals. Unfortunately, there is no general or standardized integration process; the nature of an integration process depends on the data type, domain, and integration purpose. Based on these parameters, we proposed, implemented, and tested a big data integration framework that integrates big data in the biology domain, based on the domain ontology and using distributed processing. The integration resulted in the same result as that obtained from the local integration. The results are equivalent in terms of the ontology size before the integration; in the number of added items, skipped items, and overlapped items; in the ontology size after the integration; and in the number of edges, vertices, and roots. The results also do not violate any logical consistency rules, passing all the logical consistency tests, such as Jena Ontology API, HermiT, and Pellet reasoners. The integration result is a new big data source that combines big data from several critical sources in the biology domain and transforms it into one unified format to help researchers and specialists use it for further research and analysis. View Full-Text
Keywords: big data; big data integration; biological big data; ontology integration; distributed integration big data; big data integration; biological big data; ontology integration; distributed integration
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MDPI and ACS Style

Almasoud, A.; Al-Khalifa, H.; Al-salman, A.; Lytras, M. A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing. Appl. Sci. 2020, 10, 7092. https://doi.org/10.3390/app10207092

AMA Style

Almasoud A, Al-Khalifa H, Al-salman A, Lytras M. A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing. Applied Sciences. 2020; 10(20):7092. https://doi.org/10.3390/app10207092

Chicago/Turabian Style

Almasoud, Ameera, Hend Al-Khalifa, AbdulMalik Al-salman, and Miltiadis Lytras. 2020. "A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing" Applied Sciences 10, no. 20: 7092. https://doi.org/10.3390/app10207092

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