In fast OLAP applications it is often advantageous to provide approximate answers to range
queries in order to achieve very high performances. A possible solution is to inquire
summary data rather than the original ones and to perform suitable interpolations.
Approximate answers become mandatory in situations where only aggregate data are
available. This paper studies the problem of estimating range queries (namely, sum and
count) over aggregate data using a probabilistic approach for computing expected value …

The problem of recovering (count and sum) range queries over multidimensional data only
on the basis of aggregate information on such data is addressed. This problem can be
formalized as follows. Suppose that a transformation T producing a summary from a
multidimensional data set is used. Now, given a data set D, a summary S= T (D) and a range
query r on D, the problem consists of studying r by modelling it as a random variable defined
over the sample space of all the data sets D'such that T (D)= S. The study of such a random …