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The Scientific World Journal
Volume 2014, Article ID 370656, 11 pages
http://dx.doi.org/10.1155/2014/370656
Research Article

Constraint Violations in Stochastically Generated Data: Detection and Correction Strategies

1Department of Accounting and Information Systems, Qatar University, P.O. Box 2713, Doha, Qatar
2Department of Computer and Information Science, Cleveland State University, Cleveland, OH 44114, USA

Received 9 August 2013; Accepted 19 October 2013; Published 4 February 2014

Academic Editors: P. Bala and Y.-P. Huang

Copyright © 2014 Adam Fadlalla and Toshinori Munakata. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We 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.