Selectivity Estimation for Exclusive Query Translation in Deep Web Data Integration

Abstract

Abstract. In Deep Web data integration, some Web database interfaces express exclusive predicates of the form Qe = Pi (Pi ∈ P1, P2, . . . , Pm), which permits only one predicate to be selected at a time. Accurately and efficiently estimating the selectivity of each Qe is of critical importance to optimal query translation. In this paper, we mainly focus on the selectivity estimation on infinite-value attribute which is more difficult than that on key attribute and categorical attribute. Firstly, we compute the attribute correlation and retrieve approximate random attribute-level samples through submitting queries on the least correlative attribute to the actual Web database. Then we estimate Zipf equation based on the word rank of the sample and the actual selectivity of several words from the actual Web database. Finally, the selectivity of any word on the infinite-value attribute can be derived by the Zipf equation. An experimental evaluation of the proposed selectivity estimation method is provided and experimental results are highly accurate.

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“…Precision denotes the percentage of the correctly query translation templates over all the query translation templates, Recall denotes the percentage of the correctly query translation templates over all the query form, and F-measure is a comprehensive assessment of Precision and Recall. The formulas are defined as (4), (5) and (6)…”

“…Meanwhile, they also present an approach to construct querying capability automatically. Fangjiao Jiang et al [5] study the query translation problem of the exclusive query interface and present a novel Zipf-based selectivity estimation approach for infinite-value attribute. Experimental results on several large-scale Web databases indicate that the approach can achieve high precision on selectivity estimation of infinite-value attribute for exclusive query translation.…”