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

研究成果: 書貢獻/報告類型會議貢獻

22 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014
發行者Institute of Electrical and Electronics Engineers Inc.
頁面281-287
頁數7
ISBN(列印)9781479950577
DOIs
出版狀態已發佈 - 九月 22 2014
對外發佈
事件3rd IEEE International Congress on Big Data, BigData Congress 2014 - Anchorage, 美国
持續時間: 六月 27 2014七月 2 2014

其他

其他3rd IEEE International Congress on Big Data, BigData Congress 2014
國家/地區美国
城市Anchorage
期間6/27/147/2/14

ASJC Scopus subject areas

  • 電腦科學應用

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