Next Article in Journal
Local Entropy Generation in Compressible Flow through a High Pressure Turbine with Delayed Detached Eddy Simulation
Previous Article in Journal
Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy
Open AccessTechnical Note

Comparing Relational and Ontological Triple Stores in Healthcare Domain

Department of Computer Engineering, Ege University, 35100 Bornova-Izmir, Turkey
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2017, 19(1), 30;
Received: 8 December 2016 / Revised: 5 January 2017 / Accepted: 9 January 2017 / Published: 11 January 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
Today’s technological improvements have made ubiquitous healthcare systems that converge into smart healthcare applications in order to solve patients’ problems, to communicate effectively with patients, and to improve healthcare service quality. The first step of building a smart healthcare information system is representing the healthcare data as connected, reachable, and sharable. In order to achieve this representation, ontologies are used to describe the healthcare data. Combining ontological healthcare data with the used and obtained data can be maintained by storing the entire health domain data inside big data stores that support both relational and graph-based ontological data. There are several big data stores and different types of big data sets in the healthcare domain. The goal of this paper is to determine the most applicable ontology data store for storing the big healthcare data. For this purpose, AllegroGraph and Oracle 12c data stores are compared based on their infrastructural capacity, loading time, and query response times. Hence, healthcare ontologies (GENE Ontology, Gene Expression Ontology (GEXO), Regulation of Transcription Ontology (RETO), Regulation of Gene Expression Ontology (REXO)) are used to measure the ontology loading time. Thereafter, various queries are constructed and executed for GENE ontology in order to measure the capacity and query response times for the performance comparison between AllegroGraph and Oracle 12c triple stores. View Full-Text
Keywords: big health data; big data stores; triple store performance; Semantic Web; healthcare ontology big health data; big data stores; triple store performance; Semantic Web; healthcare ontology
Show Figures

Figure 1

MDPI and ACS Style

Can, O.; Sezer, E.; Bursa, O.; Unalir, M.O. Comparing Relational and Ontological Triple Stores in Healthcare Domain. Entropy 2017, 19, 30.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop