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<p>One of the important issues in range query (RQ) retrieval problems is to determine the key's resolution for multiattribute records. Conventional models need to be improved because of potential degeneracy, less desired computability, and possible inconsistency with the partial match query (PMQ) models. This paper presents a new RQ model to overcome these drawbacks and introduces a new methodology, <it>stochastic programming</it> (SP), to conduct the optimization process. The model is established by using a monotone increasing function to characterize range sizes. Three SP approaches, <it>wait-and-see </it> (WS), <it>here-and-now</it> (HN), and <it>scenario tracking</it> (ST) methods are integrated into this RQ model. Analytical expressions of the optimal solution are derived. It seems that HN has advantage over WS because the latter usually involves complicated multiple summations or integrals. For the ST method, a nonlinear programming software package is designed. Results of numerical experiments are presented that optimized a 10-dimensional RQ model and tracked a middle size (100) and a large size (1,000) scenarios.</p>
Multiattribute hashing, partial match query, physical data organization, range query, stochastic programming.

X. Liu and W. Xu, "A Stochastic Programming Approach for Range Query Retrieval Problems," in IEEE Transactions on Knowledge & Data Engineering, vol. 14, no. , pp. 867-880, 2002.
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