An efficient algorithm to rank the partial-sum queries results

In decision-support systems, ranking-aware queries have been gaining much attention recently. In this paper we introduce an efficient algorithm about ranking the results of partial-sum queries in OLAP data cubes. We propose a term, partial-sum ranking query, which aggregate information over some specified cells, and return the ranked order of the aggregated values. Partial-sum ranking query differs from partial-sum query in that partial-sum ranking query needs to rank the results of partial-sum queries to answer what are the top-k values of a certain dimension based on the aggregates over partial cells in other dimensions. It is similar to ranking the results of range-sum queries but the cells covered by partial-sum ranking queries are not continuous. We empirically evaluate our algorithm and the experimental results show that the query cost is improved significantly