Analyzing Time Variations in a Data Set

摘要

Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution. Similarly, differences in observations between two groups can be decomposed into multiple contributing sub-groups for each of the groups and pairwise differences among sub-groups can be quantified and analyzed.

主权项

1. A method for identifying causes of time variations in a data set for a process, the method comprising a computer system automatically performing the following:
processing a data set containing observations of the process taken at different times, the observations expressed as values for a plurality of variables and for the outcome, wherein processing the data set determines behaviors for different variable combinations at different times with respect to the outcome, the variable combinations defined by values for one or more of the variables; estimating time variations in the contributions of the variable combinations to the outcome, based on time variations in the behaviors of variable combinations and also based on time variations in populations of the variable combinations; and reporting time variations based on the estimated time variations for the variable combinations.