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.
D. S. Alberts, J. J. Garstka and F. P. Stein, "Network Centric Warfare: Developing and Leveraging Information Superiority," Washington D.C.: DoD CCRP Publication Series, 2000.
A. K. Cebrowski and J. J. Garstka, "Network-Centric Warfare: Its Origin and Future," U.S. Naval Institute Proceedings, vol. 124, no. 1, pp. 28-35, 1998.
US Joint Staff Director , "CJCSI 6212.01F Net Ready Key Performance Parameter (NR KPP)," 21 March 2012. [Online]. Available: http:\\www.dtic.mil\cjcs_directives/cdata/unlimit/6212_01.pdf.
DoD CIO. Department of Defense Directive 8320.02, "Data Sharing in a Net-Centric Department of Defense," 2 December 2004.
DoDI 8320.07, "Implementing the Sharing of Data, Information, and Information Technology (IT) Services in the Department of Defense," 3 August 2015. [Online]. Available: http://www.dtic.mil/whs/directives/corres/832007p.pdf.
DoD CIO. U. S. Department of Defense Guide 8320.02., "Guidance for Implementing Net-Centric Data Sharing," 12 April 2006.
U. S. DoD, "Manual for the Operation of the Joint Capabilities Integration and Development System," 12 February 2015.
J. G. March and R. and Weissinger-Baylon, Ambiguity and Control: Organization Perspective on Military Decision Making, Pitman Publishing, 1986.
Rec. ITU-T P.10/G.100 Ammendment 1 (01/07): New Appendix I, Definition of Quality of Experience (QoE), Geneva: Int. Telcomm. Union, 2007.
D. G. Horvitz and D. J. Thompson, "A generalization of sampling without replacement from a finite universe," Journal of the American Statistical Association, vol. 47, no. 260, pp. 663-685, 1952.
J. Evans and C. Filsfils, Deploying IP and MPLS QoS For Multiservice Networks: Theory and Practice, San Francisco, CA: Morgan Kaufmann, 2010.
M. Alreshoodi and J. Woods, "Survey on QoE\QoS correlation models for multimedia services," arXiv preprint arXiv: 1306.0221, 2013.
Rec. ITU-T P.862.1, Mapping function for transforming P.862 raw results to MOS-LQO, Geneva: Int. telecomm. Union, 2003.
Rec. ITU-T J.144 Objective perceptual video quality measurement techniques for digital cable television in the presence of a full reference, Geneva : Int. Telecomm. Union, 2001.
MISB RP 1203.3 Video Interpretability and Quality Measurement and Prediction, Motion Imagery Standards Board, 2014.
Rec. ITU-T P.912: Subjective video quality assessment methods for recognition tasks, Geneva: Int. Telecomm. Union, 2008.
J. C. Leachtenauer, W. Malila, J. Irvine, L. Colburn and N. Salvaggio, "General image-quality equation: GIQE," Applied Optics, vol. 36, no. 32, pp. 8322-8328, 1997.
MISB ST 901.2 Video-National Imagery Interpretability Rating Scale, Motion Imagery Standards Board, 2014.
R. Serral-Gracia, E. Cerqueira, M. Curado, M. Yannuzzi, E. Monteiro and X. Masip-Bruin, "An overview of quality of experience measurement challenges for video applications in IP networks," Wired/Wireless Internet Communications, pp. 252-263, 2010.
J. Zhang and N. Ansari, "On assuring end-to-end QoE in next generation networks: challenges and a possible solution," Communications Magazine, IEEE, vol. 49, no. 7, pp. 185-191, 7 2011.
M. Fiedler, T. Hossfeld and P. Tran-Gia, "A generic quantitative relationship between quality of experience and quality of service," Network, IEEE, vol. 24, no. 2, pp. 36-41, 2010.
T. Hastie, Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
R. Hayes-Roth, "Model-based Communication Networks and VIRT: Filtering Information by Value to Improve Collaborative Decision-Making," in 10th International Command and Control Research and Technology Symposium, Monterey, CA: Naval Post Graduate School, 2005.
M. Maschler, E. Solan and S. Zamir, in Game Theory, Cambridge University Press, 2013, pp. 176-180.
A. Wald, Statistical decision functions, Oxford: Wiley, 1950.