A compression method for prefix-sum cubes

Wavelet-based relative prefix sum methods for range sum queries in data cubes

Data mining and related applications often rely on extensive range sum queries and thus, it is important for these queries to scale well. Range sum queries in data cubes can be achieved in time O(1) using prefix sum aggregates but prefix sum update costs are proportional to the size of the data cube O ( nd ) . Using the Relative Prefix Sum (RPS) method, the update costs can be reduced to the ro...

Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes

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 ...

Hierarchical Prefix Cubes for Range-Sum Queries

Wavelet - Based Relative Prefix Sum Methods for Range Sum Queries in Data

Data mining and related applications often rely on extensive range sum queries and thus, it is important for these queries to scale well. Range sum queries in data cubes can be achieved in time O(1) using prefix sum aggregates but prefix sum update costs are proportional to the size of the data cube O ( nd ) . Using the Relative Prefix Sum (RPS) method, the update costs can be reduced to the ro...

Data Cubes in Dynamic Environments

The data cube, also known in the OLAP community as the multidimensional database, is designed to provide aggregate information that can be used to analyze the contents of databases and data warehouses. Previous research mainly focussed on strategies for supporting queries, assuming that updates do not play an important role and can be propagated to the data cube in batches. While this might be ...