Probabilistic Model for Accuracy Estimation in Approximate Monodimensional Analyses

Approximate query processing is often based on analytical methodologies able to provide fast responses to queries. As a counterpart, the approximate answers are affected with a small quantity of error. Nowadays, these techniques are being exploited in data warehousing environments, because the queries devoted to extract information involve high-cardinality relations and, therefore, require a high computational time. Approximate answers are profitably used in the decision making process, where the total precision is not needed. Thus, it is important to provide decision makers with accuracy estimates of the approximate answers; that is, a measure of how much reliable the approximate answer is. Here, a probabilistic model is presented for providing such an accuracy measure when the analytical methodology used for decisional analyses is based on polynomial approximation. This probabilistic model is a Bayesian network able to estimate the relative error of the approximate answers. Key-Words: Analytic query processing, Approximate query answer, Polynomial approximation, Accuracy estimation, Probabilistic model.