Effective stratification for low selectivity queries on deep web data sources
Fan Wang, Gagan Agrawal · 2011
We study the problem of estimating the result of an aggregation query with low selectivity when a data source only supports limited data accesses. Existing stratified sampling techniques cannot be applied to such a problem since either it is very hard, if not impossible, to gather certain critical statistics from such a data source, or more importantly, the selective attribute of the query may not be queriable on the data source. In such cases, we need an effective mechanism to stratify the data and form homogeneous strata with respect to the selective attribute of the query, despite not being able to query the data source with the selective attribute.