Ameur Boujenoui

A probabilistic model for data cube compression and query approximation

Databases and data warehouses contain an overwhelming volume of information that users must wade through in order to extract valuable and actionable knowledge to supportthe decision-making process. This contribution addresses the problem of automatically analyzing large multidimensional tables to get a concise representation of data, identify patterns and provide approximate answers to queries. Since data cubes are nothing but multi-way tables, we propose to analyze the potential of a probabilistic modeling technique, called non-negative multi-way array factorization, for approximating aggregate and multidimensional values. Using such a technique, we compute the set of components (clusters) that best fit the initial data set and whose superposition approximates the original data. The generated components can then be exploited for approximately answering OLAP queries such as roll-up, slice and dice operations. The proposed modeling technique will then be compared against the log-linear modeling technique which has already been used in the literature for compression and outlier detection in data cubes. Finally, three data sets will be used to discuss the potential benefits of non-negative multiway array factorization.

Extracting Exact and Approximate Rules from Databases

In addition to being a technique for classifying objects and defining concepts from data, the concept lattice may be exploited to discover functional dependencies as well as exact and approximate (probabilistic) implication rules between properties (descriptors). This paper presents algorithms for rule generation and shows that the lattice is an interesting framework for that purpose.