Source Selection Optimization
It is difficult for DOE researchers to keep apprised of relevant scientific information given its rapid rate of growth. No mechanism exists to automatically discover, configure access to, or select sources for searching. This project will develop a suite of applications that discover new information sources, build federated search query interfaces to them, and automatically select relevant sources for researchers to search. Phase I proved feasibility of automated discovery of information sources, of human-assisted configuration of these sources for federated search, and of automated source selection using a database of sample queries. Phase II will extend the algorithms and approaches of the Phase I work to develop a prototype federated search application that can search a large number of sources simultaneously. The sources for the prototype will be discovered and configured with the assistance of software prototyped in Phase I and extended in Phase II. Commercial Applications and other Benefits as described by the awardee: There is much commercial interest in source discovery, configuration, and in source selection at search time. Source discovery and configuration, in particular, are very time consuming tasks, thus any automation that can be applied to them will have commercial value.
Small Business Information at Submission:
Deep Web Technologies, Llc
301 North Guadalupe Suite 201 Santa Fe, NM 87501
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