Evaluating the cost of Boolean query mapping

Non-uniform query languages make searching over heterogeneous information sources difficult. Our approach is to allow a user to compose Boolean queries in one rich front-end language. For each user query and target source, we transform the user query into a subsuming query that can be supported by the source but that may return extra documents. The results are then processed by a filter query to yield the correct final results. This post-filtering approach may involve significant cost because the documents that the users will not see may have to be retrieved and filtered. There are generally two ways to implement post-filtering: batch post-filtering and incremental post-filtering. In this paper we evaluate the costs of both methods for different search features such as proximity operators. The experimental results show that in many cases incremental post-filtering cost may be acceptable, while the batch post-filtering cost may sometimes be extremely large.