Adaptive API for Real-Time Streaming Analytics as a Service.

Inibhunu, C; Jalali, R; Doyle, I; Gates, A; Madill, J; McGregor, C

Adaptive API for Real-Time Streaming Analytics as a Service.

Inibhunu, C Jalali, R Doyle, I Gates, A Madill, J McGregor, C

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2019, 2019, pp. 3472-3477
Issue Date:: 2019-07

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Field	Value	Language
dc.contributor.author	Inibhunu, C
dc.contributor.author	Jalali, R
dc.contributor.author	Doyle, I
dc.contributor.author	Gates, A
dc.contributor.author	Madill, J
dc.contributor.author	McGregor, C https://orcid.org/0000-0002-0491-4403
dc.date	2019-07-23
dc.date.accessioned	2020-06-18T20:44:52Z
dc.date.available	2020-06-18T20:44:52Z
dc.date.issued	2019-07
dc.identifier.citation	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2019, 2019, pp. 3472-3477
dc.identifier.isbn	978-1-5386-1311-5
dc.identifier.issn	1557-170X
dc.identifier.issn	1558-4615
dc.identifier.uri	http://hdl.handle.net/10453/141539
dc.description.abstract	A significant amount of physiological data is generated from bedside monitors and sensors in neonatal intensive units (NICU) every second, however facilitating the ingestion of such data into multiple analytical processes in a real time streaming architecture remains a central challenge for systems that seek effective scaling of real-time data streams. In this paper we demonstrate an adaptive streaming application program interface (API) that provides real time streams of data for consumption by multiple analytics services enabling real-time exploration and knowledge discovery from live data streams. We have designed, developed and evaluated an adaptive API with multiple ingestion of data streamed out of bedside monitors that is passed to a middleware for standardization and structuring and finally distributed as a service for multiple analytical services to consume and perform further processing. This approach allows, (a) multiple applications to process the same data streams using multiple algorithms, (b) easy scalability to manage diverse data streams, (c) processing of analytics for each patient monitored at the NICU, (d) ability to integrate analytics that seek to evaluate multiple patients at the same point in time, and (e) a robust automated process with no manual interruptions that effectively adapts to changing data volumes when bedside monitors increases or the amount of data emitted by a monitor changes. The proposed architecture has been instantiated within the Artemis Platform which provides a framework for real-time high speed physiological data collection from multiple and diverse bed side monitors and sensors in NICUs from multiple hospitals. Results indicate this is a robust approach that can scale effectively as data volumes increase or data sources change.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.relation.isbasedon	10.1109/embc.2019.8856602
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject.mesh	Algorithms
dc.subject.mesh	Electronic Data Processing
dc.subject.mesh	Humans
dc.subject.mesh	Monitoring, Physiologic
dc.subject.mesh	Software
dc.title	Adaptive API for Real-Time Streaming Analytics as a Service.
dc.type	Conference Proceeding
utslib.citation.volume	2019
utslib.location.activity	Berlin, Germany
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-18T20:44:49Z
pubs.finish-date	2019-07-27
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-07-23
pubs.volume	2019
dc.location	Piscataway, USA

Abstract:

A significant amount of physiological data is generated from bedside monitors and sensors in neonatal intensive units (NICU) every second, however facilitating the ingestion of such data into multiple analytical processes in a real time streaming architecture remains a central challenge for systems that seek effective scaling of real-time data streams. In this paper we demonstrate an adaptive streaming application program interface (API) that provides real time streams of data for consumption by multiple analytics services enabling real-time exploration and knowledge discovery from live data streams. We have designed, developed and evaluated an adaptive API with multiple ingestion of data streamed out of bedside monitors that is passed to a middleware for standardization and structuring and finally distributed as a service for multiple analytical services to consume and perform further processing. This approach allows, (a) multiple applications to process the same data streams using multiple algorithms, (b) easy scalability to manage diverse data streams, (c) processing of analytics for each patient monitored at the NICU, (d) ability to integrate analytics that seek to evaluate multiple patients at the same point in time, and (e) a robust automated process with no manual interruptions that effectively adapts to changing data volumes when bedside monitors increases or the amount of data emitted by a monitor changes. The proposed architecture has been instantiated within the Artemis Platform which provides a framework for real-time high speed physiological data collection from multiple and diverse bed side monitors and sensors in NICUs from multiple hospitals. Results indicate this is a robust approach that can scale effectively as data volumes increase or data sources change.

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http://hdl.handle.net/10453/141539