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Our Knowledge Management Hubble May Need Glasses: Designing for Unknown Real-Time Big Data System Faults

Our Knowledge Management Hubble May Need Glasses: Designing for Unknown Real-Time Big Data System Faults

William H. Money, Stephen J. Cohen
Copyright: © 2018 |Volume: 14 |Issue: 1 |Pages: 21
ISSN: 1548-0666|EISSN: 1548-0658|EISBN13: 9781522542568|DOI: 10.4018/IJKM.2018010103
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MLA

Money, William H., and Stephen J. Cohen. "Our Knowledge Management Hubble May Need Glasses: Designing for Unknown Real-Time Big Data System Faults." IJKM vol.14, no.1 2018: pp.30-50. http://doi.org/10.4018/IJKM.2018010103

APA

Money, W. H. & Cohen, S. J. (2018). Our Knowledge Management Hubble May Need Glasses: Designing for Unknown Real-Time Big Data System Faults. International Journal of Knowledge Management (IJKM), 14(1), 30-50. http://doi.org/10.4018/IJKM.2018010103

Chicago

Money, William H., and Stephen J. Cohen. "Our Knowledge Management Hubble May Need Glasses: Designing for Unknown Real-Time Big Data System Faults," International Journal of Knowledge Management (IJKM) 14, no.1: 30-50. http://doi.org/10.4018/IJKM.2018010103

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Abstract

This article analyzes the properties of unknown faults in knowledge management and Big Data systems processing Big Data in real-time. These faults introduce risks and threaten the knowledge pyramid and decisions based on knowledge gleaned from volumes of complex data. The authors hypothesize that not yet encountered faults may require fault handling, an analytic model, and an 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 the data. New architectures, methods, and tools for handling and analyzing Big Data systems functioning in real-time will contribute to organizational knowledge and performance. System designs must mitigate faults resulting from real-time streaming processes while ensuring that variables such as synchronization, redundancy, and latency are addressed. This article concludes that with improved designs, real-time Big Data systems may continuously deliver the value of streaming Big Data.

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