The Eeects of Query-based Sampling on Automatic Database Selection Algorithms Keywords: Distributed Collections, Merging Search Results/information Synthesis, Database Selection

Database selection algorithms need to know the subject areas covered by each text database, but this metadata can be diicult to acquire in multi-party environments, such as the Internet, where each party has diierent interests and capabilities. Query-based sampling is a relatively new technique in which metadata is inferred by interacting with each text database and observing the outcomes. Query-based sampling has been proposed as a solution to the problem of discovering the contents of each database in multi-party environments, but its generality and eeectiveness had not been tested under a wide range of conditions. This paper investigates the generality and eeectiveness of query-based sampling with three well-known database selection algorithms (gGlOSS, CORI, CVV). Experimental results support the generality of query-based sampling as a solution for acquiring database descriptions in multi-party environments. The experiments also compare the eeectiveness of the database selection algorithms under diierent conditions.