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A user-friendly tool to transform large scale administrative data into wide table format using a MapReduce program

Hiromasa Horiguchi1, Hideo Yasunaga, Hideki Hashimoto

  • 1Department of Health Management and Policy, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1138555, Japan. hiromasa-tky@umin.ac.jp

BMC Medical Informatics and Decision Making
|December 25, 2012
PubMed
Summary
This summary is machine-generated.

A new open-source system simplifies using MapReduce technology for large-scale health data analysis. It efficiently transforms complex data into a usable format, reducing processing time and improving scalability for researchers.

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Area of Science:

  • Health Services Research
  • Bioinformatics
  • Data Science

Background:

  • Secondary use of large-scale administrative data is crucial for health services and clinical research.
  • Existing MapReduce tools like Hadoop lack user-friendly functions for transforming data into wide table formats.
  • A demand exists for efficient data management tools in health research.

Purpose of the Study:

  • To develop a user-friendly system for transforming large-scale administrative data for health research.
  • To enhance the capabilities of MapReduce technology for data management in clinical research.
  • To create an open-source tool for efficient data processing.

Main Methods:

  • Developed a novel system, GroupFilterFormat, using Pig Latin scripts for field and data content definition.
  • Created user-defined functions for table format transformation and date condition processing.
  • Utilized Amazon Inc.'s Elastic Compute Cloud for processing speed and scaling benchmarks with large datasets.

Main Results:

  • Demonstrated significant reduction in response time for data processing.
  • Observed a linear relationship between data quantity and processing time.
  • Showcased clear scalability, with a 47% decrease in processing time when doubling nodes.

Conclusions:

  • The developed system is an accessible, open-source resource for researchers.
  • It simplifies the application of MapReduce technology for large-scale administrative health data.
  • The system shows promise for efficient data processing in health services and clinical research.