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This paper studies optimizing top-k queries in middleware. While many assorted algorithms have been proposed, none is generally applicable to a wide range of possible scenarios. Existing algorithms lack "Generality" to support a wide range of access scenarios and systematic "Adaptivity" to account for runtime specifics. To fulfill this critical lacking, the authors aim at taking a cost-based optimization approach: By runtime search over a space of algorithms, cost-based optimization is general across a wide range of access scenarios, yet adaptive to the specific access costs at runtime. While such optimization has been taken for granted for relational queries from early on, it has been clearly lacking for ranked queries.
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