Augmenting OLAP exploration with dynamic advanced analytics

Online Analytical Processing (OLAP) is a popular technique for explorative data analysis. Usually, a fixed set of dimensions (such as time, place, etc.) is used to explore and analyze various subsets of a given, multi-dimensional data set. These subsets are selected by constraining one or several of the dimensions, for instance, showing sales only in a given year and geographical location. Still, such aggregates are often not enough. Important information can only be discovered by combining several dimensions in a multidimensional analysis. Most existing approaches allow to add new dimensions either statically or dynamically. These approaches support, however, only the creation of global dimensions that are not interactive for the user running the report. Furthermore, they are mostly restricted to data clustering and the resulting dimensions cannot be interactively refined. In this paper we propose a technique and an architectural solution that is based on an interaction concept for c...

• Type: Conference
• Year: 2010
• Where: EDBT
• Authors: Benjamin Leonhardi, Bernhard Mitschang, Rubén Pulido, Christoph Sieb, Michael Wurst
• Keywords: Data Mining, Database, Dimensions, EDBT 2010, Multidimensional Analysis