View discovery in OLAP databases through statistical combinatorial optimization

Abstract

The capability of OLAP database software systems to handle data complexity comes at a high price for analysts, presenting them a combinatorially vast space of views of a relational database. We respond to the need to deploy technologies sufficient to allow users to guide themselves to areas of local structure by casting the space of views of an OLAP database as a combinatorial object of all projections and subsets, and view discovery as an search process over that lattice. We equip the view lattice with statistical information theoretical measures sufficient to support a combinatorial optimization process. We outline hop-chaining as a particular view discovery algorithm over this object, wherein users are guided across a permutation of the dimensions by searching for successive two-dimensional views, pushing seen dimensions into an increasingly large background filter in a spiraling search process. We illustrate this work in the context of data cubes recording summary statistics for radiation portal monitors at US ports. © 2009 Springer Berlin Heidelberg.