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Quantitative models are needed for a variety of management tasks, including (a) identification of critical variables to use for health monitoring, (b) anticipating service level violations by using predictive models, and (c) on-going optimization of configurations. Unfortunately, constructing quantitative models requires specialized skills that are in short supply. Even worse, rapid changes in provider configurations and the evolution of business demands mean that quantitative models must be updated on an on-going basis. This paper describes an architecture and algorithms for on-line discovery of quantitative models without prior knowledge of the managed elements. The architecture makes use of an element schema that describes managed elements using the common information model (CIM). Algorithms are presented for selecting a subset of the element metrics to use as explanatory variables in a quantitative model and for constructing the quantitative model itself. We further describe a prototype system based on this architecture that incorporates these algorithms. We apply the prototype to on-line estimation of response times for DB2 Universal Database under a TPC-W workload. Of the approximately 500 metrics available from the DB2 performance monitor, our system chooses 3 to construct a model that explains 72% of the variability of response time.