Query-based sampling of text databases
The proliferation of searchable text databases on corporate networks and the Internet causes a database selection problem for many people. Algorithms such as gGlOSS and CORI can automatically select which text databases to search for a given information need, but only if given a set of resource descriptions that accurately represent the contents of each database. The existing techniques for acquiring resource descriptions have significant limitations when used in wide-area networks controlled by many parties. This paper presents query-based sampling, a new technique for acquiring accurate resource descriptions. Query-based sampling does not require the cooperation of resource providers, nor does it require that resource providers use a particular search engine or representation technique. An extensive set of experimental results demonstrates that accurate resource descriptions are created, that computation and communication costs are reasonable, and that the resource descriptions do in fact enable accurate automatic database selection.
query-based sampling algorithms experimentation design distributed information retrieval resource ranking resource selection server selection