An effective model for store and retrieve big health data in cloud computing.
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.
Cloud Research Center (CRC), Amirkabir University of Technology, Tehran, Iran. Electronic address: zh.goli22@gmail.com.
Zohreh
Z
Goli-Malekabadi
Cloud Research Center (CRC), Amirkabir University of Technology, Tehran, Iran.
Morteza
M
Sargolzaei-Javan
Cloud Research Center (CRC), Amirkabir University of Technology, Tehran, Iran.
Mohammad Kazem
MK
Akbari
D000067917:Cloud Computing; D016247:Information Storage and Retrieval; D008962:Models, Theoretical
Big data; Cloud computing; Health data; Information storage and retrieval; NoSQL; Relational database