Analyzing large amounts of data is difficult
because of the limitation of screen space and human’s perceptual processing.
Breakdown data analysis comes from information foraging theory, which guides
users’ progress from summary information to detailed data by maximizing gains
of valuable information per unit cost. In a tabular data set, aggregation is to
group or combine tuples and attributes into summaries using various operators.
In reverse, breakdown is to segragate summaries progressively into individual
tuples. Iteratively applying aggregation and breakdown generates tables with
diverse scales on different levels of abstraction, which form an aggregation
space. The conceptual table-scale model offers a general framework. If
visualized, the framework supports creation and navigation through the tabular
space by providing an overview of all possibilities, visual representations of
breakdown results and aggregation paths.
Qing Li, Chris North, A Survey on Aggregation Strategies in Information Visualization, submitted to Information Visualization Journal, 2005
Qing Li, “Table-Scale Framework for Navigating Large Tabular Data”, PhD Proposal, 2005