Abstract: Multidimensional data analysis applications, including OLAP applications, simultaneously aggregate across many sets of dimensions. However, computing multidimensional aggregates is a performance bottleneck for OLAP data analysis applications. In order to improve the speed of interactive analysis, OLAP databases often precompute aggregates at various levels of detail and on various combinations of data attributes. However, the cost and speed of precomputation influences how frequently the aggregates can be brought up-to-date. Systems and methods disclosed herein provide graph based multidimensional analysis processing without pre-aggregating or precomputing the data along dimensional hierarchies, and by providing the results to the end user on-demand. Since preaggregation or precomputation of data along dimensional hierarchies is not necessary, implementations allow the end user to perform data analysis as soon as the data is available.

Abstract: Multidimensional data analysis applications, including OLAP applications, simultaneously aggregate across many sets of dimensions. However, computing multidimensional aggregates is a performance bottleneck for OLAP data analysis applications. In order to improve the speed of interactive analysis, OLAP databases often precompute aggregates at various levels of detail and on various combinations of data attributes. However, the cost and speed of precomputation influences how frequently the aggregates can be brought up-to-date. Systems and methods disclosed herein provide graph based multidimensional analysis processing without pre-aggregating or precomputing the data along dimensional hierarchies, and by providing the results to the end user on-demand. Since preaggregation or precomputation of data along dimensional hierarchies is not necessary, implementations allow the end user to perform data analysis as soon as the data is available.

Abstract: Multidimensional data analysis applications, including OLAP applications, simultaneously aggregate across many sets of dimensions. However, computing multidimensional aggregates is a performance bottleneck for OLAP data analysis applications. In order to improve the speed of interactive analysis, OLAP databases often precompute aggregates at various levels of detail and on various combinations of data attributes. However, the cost and speed of precomputation influences how frequently the aggregates can be brought up-to-date. Systems and methods disclosed herein provide graph based multidimensional analysis processing without pre-aggregating or precomputing the data along dimensional hierarchies, and by providing the results to the end user on-demand. Since preaggregation or precomputation of data along dimensional hierarchies is not necessary, implementations allow the end user to perform data analysis as soon as the data is available.