Cloud Medical Data System Sharding for Fast Query Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems face challenges in efficiently organizing, storing, and distributing vast quantities of biological information for individual patients, particularly in clinical settings, where rapid access and secure query processing are essential for medical diagnosis and treatment.
Innovation Solution
A cloud-like medical-information system is implemented, utilizing network-like data structures to store and organize patient data and clinical knowledge, enabling rapid query processing and secure information exchange through encryption-based techniques, allowing for efficient retrieval and distribution of genomics information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vast quantities of biological information are stored in cloud-computing-based data centers, then information capacity and accessibility are improved, but query processing time and system complexity increase
Solution Approach 1:
The clinical-knowledge database is divided into multiple sharded partitions distributed across different servers. Each shard contains a subset of the clinical actions, biological elements, and variants, allowing parallel query processing and reducing the time to search through the entire database.
Solution Approach 2:
The system pre-processes and indexes clinical knowledge into a structured format with pre-computed relationships between clinical actions, biological elements, and variants. This preliminary organization enables rapid query resolution without requiring complex real-time computations.
2Reliability
If secure-computing-networking protocols are implemented for cloud data centers, then security and reliability are improved, but transaction processing speed decreases
Solution Approach 1:
The system uses encryption-based techniques to secure information exchange while maintaining query processing efficiency. encrypted data can be transmitted and processed without decryption, allowing secure communication protocols to be implemented without significantly impacting transaction processing speed.
3Ease of operation
If cloud-computing facilities are made accessible through mobile devices, then user accessibility and reach are improved, but network bandwidth requirements and system load increase
Solution Approach 1:
The system extracts and returns only the specific clinical information needed to answer each query, rather than transmitting entire datasets. This selective information retrieval reduces network bandwidth requirements and allows mobile devices to access cloud facilities efficiently.
Data Source
AI summary
The current document is directed to methods and systems for organizing, storing, searching, aggregating, and distributing large quantities of biological information obtained for individual patients. In one described implementation, the knowledge and information is stored, in data-storage facilities within cloud-computing-like systems, as clinical actions, biological elements, and variants that are logically linked together to form network-like data structures. Individual patient data and clinical-knowledge databases, including the network-like clinical-knowledge data structures, are hosted in cloud-computing-like data centers along with a variety of services that receive and process queries from users, medical-service providers, and electronic-health-record-based applications and that return requested information to the requesting entities. Despite the enormous amounts of patient data and clinical knowledge that may be stored in the cloud-computing-like data centers, certain implementations of the currently disclosed systems return responses to medical-information queries in under a second, with other implementations providing even faster query-processing turnaround times.