Flexible Query Answering in Data Cubes

Abstract

This paper presents a new approach toward approximate query answering in data warehouses. The approach is based on an adaptation of rough set theory to multidimensional data, and offers cube exploration and mining facilities.

Since data in a data warehouse come from multiple heterogeneous sources with various degrees of reliability and data formats, users tend to be more tolerant in a data warehouse environment and prone to accept some information loss and discrepancy between actual data and manipulated ones.

The objective of this work is to integrate approximation mechanisms and associated operators into data cubes in order to produce views that can then be explored using OLAP or data mining techniques. The integration of data approximation capabilities with OLAP techniques offers additional facilities for cube exploration and analysis.

The proposed approach allows the user to work either in a restricted mode using a cube lower approximation or in a relaxed mode using cube upper approximation. The former mode is useful when the query output is large, and hence allows the user to focus on a reduced set of fully matching tuples. The latter is useful when a query returns an empty or small answer set, and hence helps relax the query conditions so that a superset of the answer is returned. In addition, the proposed approach generates classification and characteristic rules for prediction, classification and association purposes.