Abstract

OLAP platforms are quite spread nowadays covering a lot of application domains. Supporting decision making activities or proving high-level structured information for reporting, they have been assumed a very important role in any organization. As their importance raise, OLAP systems administrators begin to worried a little bit more than before about the huge resources consumed with hyper-cubes (cubes, for short) implementation and exploitation. Cubes are usually designed and materialized according to the analysis requisites of decision-making agents. Without knowing a priori what data they will use, for a specific period of time, OLAP systems administrators don't have any other chance than to materialize all the data required by them. As time passes, it is easy to show that a great part of the cubes are simply not used – clearly, cubes' data is oversized. In this seminar we will present some methods and techniques to evaluate the usage of an OLAP system, and establish OLAP usage profiles based on users' querying sessions. Combining querying analysis with OLAP sessions, Markov chains and equivalence classes, we will show how to propose a new structure for a hyper-cube based on its exploitation. Our goal is simple: to reduce hyper-cube materialization processing time and storage, providing smaller cubes with the same satisfaction level. Area: On-Line Analytical Processing (OLAP)Keywords: OLAP Usage Profiling, OLAP Sessions, Markov Chains, Equivalence Classes, and Hyper-Cube Selection and Restructuring.