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

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 pre-processing-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.