International Journal on Data Science and Technology
Volume 2, Issue 6, November 2016, Pages: 76-83
Received: Aug. 15, 2016;
Accepted: Nov. 19, 2016;
Published: Dec. 21, 2016
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Lee P. Battle, Maritime Patrol and Reconnaissance Aircraft Program Office, NAVAIR, Patuxent River, USA; SAVVEE Consulting, Lexington Park, USA
Edward F. Harrington, Maritime Patrol and Reconnaissance Aircraft Program Office, NAVAIR, Patuxent River, USA; Defence Science and Technology Group, Canberra, Australia
This paper introduces a framework to determine data quality on enterprise networks for net-centric and net-ready initiatives as introduced by the US Department of Defense (DoD). Traditionally quality of data delivered to an enterprise user focuses on network performance, i.e. quality of service (QoS). It is proposed to add two new attributes pertaining to data sharing performance to QoS: data relevance (DR) and quality of data at source (QDS); and further a method to evaluate these new attributes. The QDS attribute brings distinction to the resultant data quality of the network's quality of service. This distinction is necessary to reflect the separation in procurement and management for sensor systems and network systems for the DoD. The DR attribute is introduced; it is important in enabling enterprise data consumers to sort, filter and prioritize data. There is also a need to assess the quality of data sharing across the enterprise network. One recent method subjectively assess the quality of data is to measure the user satisfaction referred to as quality of experience (QoE). The QoE is assessed for each of the framework’s attributes using the best practices from survey statistics in sampling and estimation. The overall value of data quality on enterprise networks is decided using a minimax decision model consisting of the three attributes. The resultant minimax value correlates to the lowest performing attributes of the framework. The minimax decision model is chosen to meet the design philosophy that little advantage to the overall enterprise network performance will result from further investment in high performing attributes prior to balancing performance across all three model attributes. The presented framework offers decision support tools to enable agencies to allocate limited resources towards improving the performance of their net-centric service offerings to the enterprise network.
Lee P. Battle,
Edward F. Harrington,
A Framework for Evaluating Data Quality on Military Enterprise Networks, International Journal on Data Science and Technology.
Vol. 2, No. 6,
2016, pp. 76-83.
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