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

doi:10.1109/BigData.Congress.2014.48

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 proceeding › Conference contribution

28 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	281-287
Number of pages	7
ISBN (Print)	9781479950577
DOIs	https://doi.org/10.1109/BigData.Congress.2014.48
Publication status	Published - Sept 22 2014
Externally published	Yes
Event	3rd IEEE International Congress on Big Data, BigData Congress 2014 - Anchorage, United States Duration: Jun 27 2014 → Jul 2 2014

Other

Other	3rd IEEE International Congress on Big Data, BigData Congress 2014
Country/Territory	United States
City	Anchorage
Period	6/27/14 → 7/2/14

Keywords

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

ASJC Scopus subject areas

Computer Science Applications

Access to Document

10.1109/BigData.Congress.2014.48

Cite this

Temporal event tracing on big healthcare data analytics. / Lin, Chin Ho; Huang, Liang Cheng; Chou, Seng Cho T et al.

Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 281-287 6906791.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, CH, Huang, LC, Chou, SCT, Liu, CH, Cheng, HF & Chiang, IJ 2014, Temporal event tracing on big healthcare data analytics. in Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014., 6906791, Institute of Electrical and Electronics Engineers Inc., pp. 281-287, 3rd IEEE International Congress on Big Data, BigData Congress 2014, Anchorage, United States, 6/27/14. https://doi.org/10.1109/BigData.Congress.2014.48

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abstract = "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.",

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author = "Lin, {Chin Ho} and Huang, {Liang Cheng} and Chou, {Seng Cho T} and Liu, {Chih Ho} and Cheng, {Han Fang} and Chiang, {I. Jen}",

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AU - Huang, Liang Cheng

AU - Chou, Seng Cho T

AU - Liu, Chih Ho

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AU - Chiang, I. Jen

PY - 2014/9/22

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N2 - 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.

AB - 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.

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KW - data mining

KW - medical record

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Temporal event tracing on big healthcare data analytics

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Keywords

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