Approximate Query Answering in Numerical Databases MS thesis, WPI CS dept., Worcester, MA, May 1995.
Scientific databases are usually large, distributed and dynamically changing.
The efficient processing of queries in scientific databases, especially in very large numerical databases is becoming increasingly important. Previous work has focused on how to store the database and the design of index structures for the efficient access of data. Recently more and more statistical methods have been used in query optimization. Those methods essentially attempt to approximate the distribution of the attribute values in order to estimate the selectivity of query results. We introduce a new methodology that uses regression techniques to approximate the actual attribute values.