Large information technology (IT) projects such as Defense Business System (DBS) acquisitions have been experiencing an alarming rate of large cost overruns, long schedule delays, and under-delivery of specified capabilities. There are strict defense acquisition laws/regulations/policies/guidance with an abundance of review and oversights, generating a plethora of data and evidence for project progress. However, with the size and complexity of these large IT projects and sheer amount of project data they produce, there are challenges in collectively discerning these data and making successful decisions based on them. This research article develops an analytic model with Bayesian networks to orient the vast number of acquisition data and evidence to support decision making, known as the DBS Acquisition Probability of Success (DAPS) model.
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Authors: Kevin Buell, Mustafa G. Baydogan, Burhan Senturk, and James P. Kerr
The specialized nature of technology-based programs creates volumes of data on a magnitude never before seen, complicating the test and evaluation phase of acquisition. This article provides a practical solution for reducing network traffic analysis data while expediting test and evaluation. From small lab testing to full integration test events, quality of service and other key metrics of military systems and networks are evaluated. Network data captured in standard flow formats enable scalable approaches for producing network traffic analyses. Because of its compact representation of network traffic, flow data naturally scale well. Some analyses require deep packet inspection, but many can be calculated/approximated quickly with flow data, including quality-of-service metrics like completion rate and speed of service.