Answering top-k queries with multi-dimensional selections: the ranking cube approach

Abstract

Observed in many real applications, a top-k query often consists of two components to reflect a user's preference: a selection condition and a ranking function. A user may not only propose ad hoc ranking functions, but also use different interesting subsets of the data. In many cases, a user may want to have a thorough study of the data by initiating a multi-dimensional analysis of the top-k query results. Previous work on top-k query processing mainly focuses on optimizing data access according to the ranking function only. The problem of efficient answering top-k queries with multi-dimensional selections has not been well addressed yet.This paper proposes a new computational model, called ranking cube, for efficient answering top-k queries with multi-dimensional selections. We define a rank-aware measure for the cube, capturing our goal of responding to multi-dimensional ranking analysis. Based on the ranking cube, an efficient query algorithm is developed which progressively retrieves data blocks until the top-k results are found. The curse of dimensionality is a well-known challenge for the data cube and we cope with this difficulty by introducing a new technique of ranking fragments. Our experiments on Microsoft's SQL Server 2005 show that our proposed approaches have significant improvement over the previous methods.