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Using Distributed Data over HBase in Big Data Analytics Platform for Clinical Services.

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Big data analytics in healthcare faces challenges. This study built an interactive platform using Hadoop Distributed File System (HDFS) and HBase, demonstrating potential for secure patient data management and analysis.

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Area of Science:

  • Health Informatics
  • Big Data Analytics
  • Database Management

Background:

  • Big data analytics (BDA) is crucial for reducing healthcare costs but faces significant challenges in data aggregation, maintenance, integration, translation, analysis, and security/privacy.
  • Implementing effective BDA platforms requires robust infrastructure and efficient data handling strategies.

Purpose of the Study:

  • To establish an interactive BDA platform using open-source software technologies for simulated patient data.
  • To evaluate the performance and usability of different big data technologies in a healthcare context.

Main Methods:

  • Construction of a platform framework utilizing Hadoop Distributed File System (HDFS) and HBase (a key-value NoSQL database).
  • Generation of distributed data structures from hospital-specific metadata encompassing billions of patient records.
  • Inclusion and evaluation of Apache Spark and Drill for performance and usability assessments.

Main Results:

  • HDFS ingestion to HBase demonstrated sustained availability over hundreds of iterations.
  • MapReduce to HBase processing times were substantial (one week for 10 TB, one month for 30 TB), with identified inconsistencies limiting efficient data generation and replication.
  • Apache Spark and Drill offered high performance for technical support but limited usability for clinical services.
  • Challenges were encountered in fully integrating complex patient-to-hospital relationships within HBase for patient-centric data.

Conclusions:

  • HBase is recommended for achieving secure patient data management and querying large hospital datasets.
  • A simplified clinical event model can facilitate querying across clinical services.
  • Further optimization is needed to address data integration complexities and improve efficiency for large-scale healthcare BDA.