Discovery-Driven Exploration of Data Cubes
Hypothesis-driven: exploration by user, huge search space
Discovery-driven (Sarawagi et al.’98)
- pre-compute measures indicating exceptions, guide user in the data analysis, at all levels of aggregation
- Exception: significantly different from the value anticipated, based on a statistical model
- Visual cues such as background color are used to reflect the degree of exception of each cell
- Computation of exception indicator (modeling fitting and computing SelfExp, InExp, and PathExp values) can be overlapped with cube construction