MrCoM: A Cost Model for Range Query Translation in Deep Web Data Integration

Abstract

Due to the autonomy of web databases, a major challenge for query translation in a Deep Web Data Integration System is the lack of cost models at the global level. In this paper, we propose a Multiple-regression Cost Model (MrCoM) based on statistical analysis for global range queries that involve numeric range attributes. Using the MrCoM, the query translation strategy for new global range queries can be inferred. We also propose a Preprocessing-based Stepwise Algorithm (PSA) for selecting significant independent variables into the MrCoM. Experimental results demonstrate that the fitness of the MrCoM is good and the accuracy of the query strategy selection is high.

Deep Web Query Interface Understanding and Integration

“…However, beyond a small study [Jiang et al, 2008], there is little published work in examining this issue more systematically and in evaluating the performance of different choices using large-scale datasets. Nonetheless, this is an under researched issue.…”

Deep Web Information Retrieval Process

“…Another method of query translation is proposed by Fangjiao Jiang, Linlin Jia, Weiyi Meng, Xiaofeng Meng which is based on a cost model for range query translation (Jiang et al, 2008) in deep web data integration. This paper proposes a multiple regression cost model based on statistical analysis for global range queries that involve numeric range attributes.…”