AIM: Supporting Efficient Top-k Query Processing


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With the massive amount of data everywhere, database systems are facing new challenges: to support non-traditional fuzzy retrieval, in contrast to the Boolean true/false of SQL queries, for returning best matches in a ranking of results. That is, even for structured data, we need a retrieval system, much like a ''Google'' for relational databases. Our goal is to support ranking queries, or top-k queries, for matching data by "soft" conditions such as similarity, relevance, or preference, in order to return best k answers. Such ranking queries order results by combining the scores of fuzzy predicates that are evaluated by different sources, which can be a local database, a multimedia subsystem, or a Web source. This project aims at developing semantics, algorithms, and systems for effective ranking query processing. Specifically, we address the following challenges, corresponding to four major barriers in realizing our goals.

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