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AuthorFadlalla, Adam
AuthorMunakata, Toshinori
Available date2016-03-20T11:22:55Z
Publication Date2014-02
Publication NameThe Scientific World Journal
ResourceScopus
CitationAdam Fadlalla and Toshinori Munakata, �Constraint Violations in Stochastically Generated Data: Detection and Correction Strategies,� The Scientific World Journal, vol. 2014, Article ID 370656, 11 pages, 2014.
ISSN1537-744X
URIhttp://dx.doi.org/10.1155/2014/370656
URIhttp://hdl.handle.net/10576/4248
AbstractWe consider the generation of stochastic data under constraints where the constraints can be expressed in terms of different parameter sets. Obviously, the constraints and the generated data must remain the same over each parameter set. Otherwise, the parameters and/or the generated data would be inconsistent. We consider how to avoid or detect and then correct such inconsistencies under three proposed classifications: (1) data versus characteristic parameters, (2) macro- versus microconstraint scopes, and (3) intra- versus intervariable relationships. We propose several strategies and a heuristic for generating consistent stochastic data. Experimental results show that these strategies and heuristic generate more consistent data than the traditional discard-and-replace methods. Since generating stochastic data under constraints is a very common practice in many areas, the proposed strategies may have wide-ranging applicability.
Languageen
PublisherHindawi Publishing Corporation
SubjectStochastic data
Queuing problem
Fluid dynamics
TitleConstraint violations in stochastically generated data: Detection and correction strategies
TypeArticle
Volume Number2014
dc.accessType Open Access


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