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Mohamed A. Soliman Ph.D. Student at the School of Computer Science University of Waterloo |
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is the first relational database system to study the integration of conventional ranking queries into uncertain and probabilistic databases. The URank project introduces new probabilistic formulations for ranking and ranking-aggregate queries. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics. These formulations have triggered the design of novel techniques to support different probabilistic ranking queries, and leverage existing query processing and indexing capabilities in current RDBMSs to recognize and handle data uncertainty in score-based ranking.
is a new mashup system building on concepts from rank-aware processing, probabilistic databases, and information extraction to enable ranked mashups of (unstructured) sources with uncertain ranking attributes. MashRank interleaves extraction with query processing by running information extraction programs against live Web sources, while asynchronously pushing extracted data into pipelined rank-aware relational query plans to limit extraction cost, and produce results in an early-out fashion.![]() Configuring Web source | ![]() Building source wrapper | ![]() Joining Web sources | ![]() Rank join processing |