Top-k selection queries over relational databases: Mapping strategies and performance evaluation
In many applications, users specify target values for certain attributes, without requiring exact matches to these values in return. Instead, the result to such queries is typically a rank of the "top k" tuples that best match the given attribute values. In this paper, we study the advantages and limitations of processing a top-k query by translating it into a single range query that a traditional relational database management system (RDBMS) can process efficiently. In particular, we study how to determine a range query to evaluate a top-k query by exploiting the statistics available to an RDBMS, and the impact of the quality of these statistics on the retrieval efficiency of the resulting scheme. We also report the first experimental evaluation of the mapping strategies over a real RDBMS, namely over Microsoft's SQL Server 7.0. The experiments show that our new techniques are robust and significantly more efficient than previously known strategies requiring at least one sequential scan of the data sets.
performance algorithms measurement multidimensional histograms top-k query processing