Real-time Multi-granular Analytics Framework for HIT Systems

Byung H. Park; Sangkeun Lee; Ozgur Ozmen; Mohit Kumar; Merry Ward; Jonathan R. Nebeker

doi:10.1109/BigData55660.2022.10020539

Real-time Multi-granular Analytics Framework for HIT Systems

Byung H. Park
, Sangkeun Lee
, Ozgur Ozmen
, Mohit Kumar
, Merry Ward
, Jonathan R. Nebeker

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

1 Scopus citations

Abstract

Streaming analytics is the process of ingesting and digesting live data from multiple data sources. In the healthcare domain, as the importance of extracting immediate insights while data are streaming into the system grows, the focus is shifting from batch processing to streaming analytics. With data increasing dramatically at high speeds, many informatics designs have been proposed to adapt healthcare domain into this new environment. In our previous work, we introduced a prototype of health informatics technology (HIT) framework that aims to address challenges in adopting state-of-the-art technologies to enable advanced healthcare analytic tasks in new streaming environments. We recently made major updates to the framework so that anomaly from multiple streaming data sources at different granularity levels can be detected in near real-time. In this paper, we detail the implementation and deployment of the framework in Kubernetes clusters and report its performances when tested on electronic health record (EHR) data of Veterans Affairs.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3441-3446
Number of pages	6
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020539
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: Dec 17 2022 → Dec 20 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	12/17/22 → 12/20/22

Funding

ACKNOWLEDGMENT This work is sponsored by the US Department of Veterans Affairs under Inter-Agency Agreement number VA118-17-M-2015. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Big Data
HIT
Multi-granular
Streaming Architecture

Access to Document

10.1109/BigData55660.2022.10020539

Cite this

Park, B. H., Lee, S., Ozmen, O., Kumar, M., Ward, M., & Nebeker, J. R. (2022). Real-time Multi-granular Analytics Framework for HIT Systems. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 3441-3446). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020539

Park, Byung H. ; Lee, Sangkeun ; Ozmen, Ozgur et al. / Real-time Multi-granular Analytics Framework for HIT Systems. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 3441-3446 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

@inproceedings{838106b06041458a80658ebbdc9fa5bf,

title = "Real-time Multi-granular Analytics Framework for HIT Systems",

abstract = "Streaming analytics is the process of ingesting and digesting live data from multiple data sources. In the healthcare domain, as the importance of extracting immediate insights while data are streaming into the system grows, the focus is shifting from batch processing to streaming analytics. With data increasing dramatically at high speeds, many informatics designs have been proposed to adapt healthcare domain into this new environment. In our previous work, we introduced a prototype of health informatics technology (HIT) framework that aims to address challenges in adopting state-of-the-art technologies to enable advanced healthcare analytic tasks in new streaming environments. We recently made major updates to the framework so that anomaly from multiple streaming data sources at different granularity levels can be detected in near real-time. In this paper, we detail the implementation and deployment of the framework in Kubernetes clusters and report its performances when tested on electronic health record (EHR) data of Veterans Affairs.",

keywords = "Big Data, HIT, Multi-granular, Streaming Architecture",

author = "Park, \{Byung H.\} and Sangkeun Lee and Ozgur Ozmen and Mohit Kumar and Merry Ward and Nebeker, \{Jonathan R.\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020539",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3441--3446",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and \{Van den Poel\}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

}

Park, BH , Lee, S, Ozmen, O, Kumar, M, Ward, M & Nebeker, JR 2022, Real-time Multi-granular Analytics Framework for HIT Systems. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 3441-3446, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 12/17/22. https://doi.org/10.1109/BigData55660.2022.10020539

Real-time Multi-granular Analytics Framework for HIT Systems. / Park, Byung H.; Lee, Sangkeun; Ozmen, Ozgur et al.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3441-3446 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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

TY - GEN

T1 - Real-time Multi-granular Analytics Framework for HIT Systems

AU - Park, Byung H.

AU - Lee, Sangkeun

AU - Ozmen, Ozgur

AU - Kumar, Mohit

AU - Ward, Merry

AU - Nebeker, Jonathan R.

PY - 2022

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N2 - Streaming analytics is the process of ingesting and digesting live data from multiple data sources. In the healthcare domain, as the importance of extracting immediate insights while data are streaming into the system grows, the focus is shifting from batch processing to streaming analytics. With data increasing dramatically at high speeds, many informatics designs have been proposed to adapt healthcare domain into this new environment. In our previous work, we introduced a prototype of health informatics technology (HIT) framework that aims to address challenges in adopting state-of-the-art technologies to enable advanced healthcare analytic tasks in new streaming environments. We recently made major updates to the framework so that anomaly from multiple streaming data sources at different granularity levels can be detected in near real-time. In this paper, we detail the implementation and deployment of the framework in Kubernetes clusters and report its performances when tested on electronic health record (EHR) data of Veterans Affairs.

AB - Streaming analytics is the process of ingesting and digesting live data from multiple data sources. In the healthcare domain, as the importance of extracting immediate insights while data are streaming into the system grows, the focus is shifting from batch processing to streaming analytics. With data increasing dramatically at high speeds, many informatics designs have been proposed to adapt healthcare domain into this new environment. In our previous work, we introduced a prototype of health informatics technology (HIT) framework that aims to address challenges in adopting state-of-the-art technologies to enable advanced healthcare analytic tasks in new streaming environments. We recently made major updates to the framework so that anomaly from multiple streaming data sources at different granularity levels can be detected in near real-time. In this paper, we detail the implementation and deployment of the framework in Kubernetes clusters and report its performances when tested on electronic health record (EHR) data of Veterans Affairs.

KW - Big Data

KW - HIT

KW - Multi-granular

KW - Streaming Architecture

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U2 - 10.1109/BigData55660.2022.10020539

DO - 10.1109/BigData55660.2022.10020539

M3 - Conference contribution

AN - SCOPUS:85147974774

T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 3441

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BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Big Data, Big Data 2022

Y2 - 17 December 2022 through 20 December 2022

ER -

Park BH , Lee S, Ozmen O, Kumar M, Ward M, Nebeker JR. Real-time Multi-granular Analytics Framework for HIT Systems. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3441-3446. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020539

Real-time Multi-granular Analytics Framework for HIT Systems

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