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Related Experiment Video

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Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
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Using common table expressions to build a scalable Boolean query generator for clinical data warehouses.

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    A new Boolean query generator using common-table expressions (CTEs) significantly improves performance for big data analytics in healthcare research. This scalable SQL solution outperforms existing tools, offering faster and more consistent query response times.

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    Area of Science:

    • Biomedical Informatics
    • Database Engineering

    Background:

    • Clinical research relies on efficient querying of large healthcare datasets.
    • Existing query generators in tools like Informatics for Integrating Biology and the Bedside (i2b2) can be slow and struggle with scalability.
    • Optimizing data retrieval is crucial for advancing clinical research and patient care.

    Purpose of the Study:

    • To develop and evaluate a novel Boolean query generator that leverages common-table expressions (CTEs) for enhanced scalability and performance.
    • To compare the efficiency of the CTE-based generator against the default generator within the i2b2 platform.
    • To provide a cost-effective, high-performance solution for clinical data warehousing.

    Main Methods:

    • Developed a custom Boolean query generator mapping user queries to SQL using CTEs.
    • Integrated the generator into the i2b2 query tool for practical evaluation.
    • Designed experiments with 16 query types varying date, frequency, exclusion criteria, and concept co-occurrence.
    • Compared execution times of the CTE-based generator against the default i2b2 generator.

    Main Results:

    • The CTE-based query generator significantly outperformed the default generator in execution speed.
    • Average execution time for CTEs was 2.03 seconds (SD = 6.64) compared to 75.82 seconds (SD = 238.88) for the default.
    • The CTE solution demonstrated more consistent response times across diverse query types.
    • Achieved substantial performance gains without requiring hardware upgrades.

    Conclusions:

    • Common-table expressions provide a scalable and efficient method for generating SQL queries from Boolean logic in healthcare informatics.
    • The custom CTE-based generator offers a superior alternative to default tools for clinical data warehousing, enhancing research capabilities.
    • This approach presents a promising, performance-driven solution for big data challenges in biomedical research.