Location
Hilton Waikoloa Village, Hawaii
Event Website
http://www.hicss.hawaii.edu
Start Date
1-4-2017
End Date
1-7-2017
Description
This 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.
Data Systems Fault Coping for Real-time Big Data Analytics Required Architectural Crucibles
Hilton Waikoloa Village, Hawaii
This 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.
https://aisel.aisnet.org/hicss-50/da/big_data_and_analytics/5