Temporal event tracing on big healthcare data analytics

Chin Ho Lin, Liang Cheng Huang, Seng Cho T Chou, Chih Ho Liu, Han Fang Cheng, I. Jen Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Citations (Scopus)


This study presents a comprehensive method for rapidly processing, storing, retrieving, and analyzing big healthcare data. Based on NoSQL (not only SQL), a patient-driven data architecture is suggested to enable the rapid storing and flexible expansion of data. Thus, the schema differences of various hospitals can be overcome, and the flexibility for field alterations and addition is ensured. The timeline mode can easily be used to generate a visual representation of patient records, providing physicians with a reference for patient consultation. The sharding-key is used for data partitioning to generate data on patients of various populations. Subsequently, data reformulation is conducted as a first step, producing additional temporal and spatial data, providing cloud computing methods based on query-MapReduce-shard, and enhancing the search performance of data mining. Target data can be rapidly searched and filtered, particularly when analyzing temporal events and interactive effects.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Print)9781479950577
Publication statusPublished - Sept 22 2014
Externally publishedYes
Event3rd IEEE International Congress on Big Data, BigData Congress 2014 - Anchorage, United States
Duration: Jun 27 2014Jul 2 2014


Other3rd IEEE International Congress on Big Data, BigData Congress 2014
Country/TerritoryUnited States


  • big medical data
  • data mining
  • medical record
  • NoSQL
  • shard
  • temporal event analysis

ASJC Scopus subject areas

  • Computer Science Applications


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