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Data warehousing systems enable enterprise managers to acquire and integrate information from heterogeneous sources and to query very large databases efficiently. Building a data warehouse requires adopting design and implementation techniques completely different from those underlying operational information systems. Though most scientific literature on(More)
During the last ten years the approach to business management has deeply changed, and companies have understood the importance of enforcing achievement of the goals defined by their strategy through metrics-driven management. The DW process, though supporting bottom-up extraction of information from data, fails in top-down enforcing the company strategy. A(More)
Multidimensional modeling requires specialized design techniques. Though a lot has been written about how a data warehouse should be designed, there is no consensus on a design method yet. This paper follows from a wide discussion that took place in Dagstuhl, during the Perspectives Workshop "Data Warehousing at the Crossroads", and is aimed at outlining(More)
Several surveys indicate that a significant percentage of data warehouses fail to meet business objectives or are outright failures. One of the reasons for this is that requirement analysis is typically overlooked in real projects. In this paper we propose a goal-oriented approach to requirement analysis for data warehouses, based on the Tropos methodology.(More)
Several surveys indicate that a significant percentage of data warehouses fail to meet business objectives or are outright failures. One of the reasons for this is that requirement analysis is typically overlooked in real projects. In this paper we propose GRAnD, a goal-oriented approach to requirement analysis for data warehouses based on the Tropos(More)
A major problem in map building is due to the imprecision of sensor measures. In this paper we propose a technique, called elastic correction, for correcting the dead-reckoning errors made during the exploration of an unknown environment by a robot capable of identifying landmarks. Knowledge of the environment being acquired is modelled by a relational(More)
Nowadays, the vast volume of collected digital data obliges us to employ processing methods like pattern recognition and data mining in order to reduce the complexity of data management. In this paper, we present the architecture and the logical foundations for the management of the produced knowledge artifacts, which we call patterns. To this end, we first(More)