Evaluating top-k selection queries

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 traditional relational DBMSs 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 a relational DBMS, and the impact of the quality of these statistics on the retrieval efficiency of the resulting scheme.