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المؤلفFadlalla, Adam
المؤلفMunakata, Toshinori
تاريخ الإتاحة2016-03-20T11:22:55Z
تاريخ النشر2014-02
اسم المنشورThe Scientific World Journal
المصدرScopus
الاقتباسAdam 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.
الرقم المعياري الدولي للكتاب1537-744X
معرّف المصادر الموحدhttp://dx.doi.org/10.1155/2014/370656
معرّف المصادر الموحدhttp://hdl.handle.net/10576/4248
الملخصWe 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.
اللغةen
الناشرHindawi Publishing Corporation
الموضوعStochastic data
Queuing problem
Fluid dynamics
العنوانConstraint violations in stochastically generated data: Detection and correction strategies
النوعArticle
رقم المجلد2014
dc.accessType Open Access


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