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Analytical Chem JoVE (Open Access) Citable · DOI

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

DOI: 10.3791/57439-v
What you'll learn
  • ✓ Compare query response times across relational (MySQL) and NoSQL (MongoDB, eXist) databases
  • ✓ Evaluate computational complexity scaling with increasing ISO/EN 13606 EHR dataset sizes
  • ✓ Select appropriate database architecture for standardized medical information systems
Protocol

This study compares relational and non-relational (NoSQL) standardized medical information systems. The computational complexity of the response times of querying such database management systems (DBMS) is computed using doubling-sized databases. These results help the discussion of the appropriateness of each database approach to different scenarios and problems.

Difficulty
advanced
Total time
~4–8 hours (depends on database size and query complexity; scalability testing)

Steps

1
Execute complexity-increasing queries on relational and NoSQL databases

Perform structured query operations on MySQL, MongoDB, and eXist systems populated with ISO/EN 13606 standardized EHR data. Record response times as query complexity increases.

▶ 01:17
2
Analyze and compare relational versus non-relational database performance

Evaluate computational complexity metrics and response time trends across relational and NoSQL DBMS implementations using doubling-sized datasets to assess scalability.

▶ 04:49
3
Determine optimal database approach for medical information scenarios

Synthesize performance findings to recommend appropriate database architecture based on query complexity, dataset growth, and clinical information system requirements.

▶ 06:24
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