Data Systems Fault Coping for Real-time Big Data Analytics Required Architectural Crucibles

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dc.contributor.authorCohen, Stephen
dc.contributor.authorMoney, William H.
dc.date.accessioned2016-12-29T00:27:50Z
dc.date.available2016-12-29T00:27:50Z
dc.date.issued2017-01-04
dc.description.abstractThis paper analyzes the properties and characteristics of unknown and unexpected faults introduced into information systems while processing Big Data in real-time. The authors hypothesize that there are new faults, and requirements for fault handling and propose an analytic model and architectural framework to assess and manage the faults and mitigate the risks of correlating or integrating otherwise uncorrelated Big Data, and to ensure the source pedigree, quality, set integrity, freshness, and validity of data being consumed. We argue that new architectures, methods, and tools for handling and analyzing Big Data systems functioning in real-time must design systems that address and mitigate concerns for faults resulting from real-time streaming processes while ensuring that variables such as synchronization, redundancy, and latency are addressed. This paper concludes that with improved designs, real-time Big Data systems may continuously deliver the value and benefits of streaming Big Data.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2017.121
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41274
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnalytics
dc.subjectArchitecture
dc.subjectBig-Data
dc.subjectFaults
dc.subjectRisks
dc.titleData Systems Fault Coping for Real-time Big Data Analytics Required Architectural Crucibles
dc.typeConference Paper
dc.type.dcmiText

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