Abstract
A scalable, parallel, relational database-driven information retrieval engine is described. To support portability across a wide-range of execution environments, including parallel machines, all algorithms strictly adhere to the SQL-92 standard. By incorporating relevance feedback algorithms, accuracy is enhanced over prior database-driven information retrieval efforts. Algorithmic modifications to our earlier prototype resulted in significantly enhanced scalability. Currently our information retrieval engine sustains near-linear speedups using a 24-node parallel database machine. Experiments using the TIPSTER data collections are presented to validate the described approaches.
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