An effective model for store and retrieve big health data in cloud computing. Issue 132 (August 2016)

• Record Type: Journal Article
• Authors: Goli-Malekabadi, Zohreh; Sargolzaei-Javan, Morteza; Akbari, Mohammad Kazem
• Is Part Of: Computer methods and programs in biomedicine. Issue 132(2016)
• Page Start: 75
• Page End: 82
• Publication Date: 2016-08
• Subjects: Health data -- Relational database -- NoSQL -- Big data -- Information storage and retrieval -- Cloud computing; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.04.016
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract: Background and objective: The volume of healthcare data including different and variable text types, sounds, and images is increasing day to day. Therefore, the storage and processing of these data is a necessary and challenging issue. Generally, relational databases are used for storing health data which are not able to handle the massive and diverse nature of them. Methods: This study aimed at presenting the model based on NoSQL databases for the storage of healthcare data. Despite different types of NoSQL databases, document-based DBs were selected by a survey on the nature of health data. The presented model was implemented in the Cloud environment for accessing to the distribution properties. Then, the data were distributed on the database by applying the Shard property. Results: The efficiency of the model was evaluated in comparison with the previous data model, Relational Database, considering query time, data preparation, flexibility, and extensibility parameters. The results showed that the presented model approximately performed the same as SQL Server for "read" query while it acted more efficiently than SQL Server for "write" query. Also, the performance of the presented model was better than SQL Server in the case of flexibility, data preparation and extensibility. Conclusions: Based on these observations, the proposed model was more effective than Relational Databases for handling health data.