An effective model for store and retrieve big health data in cloud computing.

Area of Science:

• Health Informatics
• Database Management Systems
• Big Data Analytics

Background:

• Healthcare data volume and diversity are rapidly increasing, posing significant storage and processing challenges.
• Traditional relational databases struggle to efficiently manage the massive and varied nature of modern health data.
• Effective data management is crucial for advancing healthcare research and clinical practice.

Discussion:

• This study introduces a novel healthcare data storage model utilizing NoSQL document-based databases.
• The model was implemented in a cloud environment, leveraging sharding for data distribution and scalability.
• NoSQL databases, particularly document-based ones, are well-suited for the heterogeneous nature of health information.

Key Insights:

• The proposed NoSQL model matches SQL Server performance for read queries.
• The NoSQL model significantly outperforms SQL Server in write query efficiency.
• The NoSQL model demonstrates superior flexibility, data preparation, and extensibility for healthcare data.

Outlook:

• This research suggests a paradigm shift towards NoSQL solutions for healthcare data infrastructure.
• Further research could explore hybrid approaches combining NoSQL and relational databases.
• Optimizing NoSQL implementations can enhance real-time analytics and personalized medicine initiatives.