Ranking bias in deep web size estimation using capture recapture method

Many deep web data sources are ranked data sources, i.e., they rank the matched documents and return at most the top k number of results even though there are more than k documents matching the query. While estimating the size of such ranked deep web data source, it is well known that there is a ranking bias—the traditional methods tend to underestimate the size when queries overflow (match more documents than the return limit). Numerous estimation methods have been proposed to overcome the ranking bias, such as by avoiding overflowing queries during the sampling process, or by adjusting the initial estimation using a fixed function.We observe that the overflow rate has a direct impact on the accuracy of the estimation. Under certain conditions, the actual size is close to the estimation obtained by unranked model multiplied by the overflow rate. Based on this result, this paper proposes a method that allows overflowing queries in the sampling process.