Summarization of intermediary query result sets plays an important role when users browse through digital library collections. Summarization enables users to quickly digest the results of their queries, and provides users with important information they can use to narrow their search interactively. Techniques from the field of data analysis may be applied to the problem of generating summaries of query results efficiently. Such techniques should permit the incorporation of classification hierarchies in order to provide powerful browsing environments for digital library users.

Range sum queries on data cubes are a powerful tool for analysis. A range sum query applies an aggregation operation (e.g., SUM) over all selected cells in a data cube, where the selection is specified by providing ranges of values for numeric dimensions. Many application domains require that information provided by analysis tools be current or "near-current." Existing techniques for range sum queries on data cubes, however, can incur update costs on the order of the size of the data cube. Since the size of a data cube is exponential in the number of its dimensions, rebuilding the entire data cube can be very costly. We present an approach that achieves constant time range sum queries while constraining update costs. Our method reduces the overall complexity of the range sum problem.