Present database systems process all the data related to a query before giving out responses. As a result, the size of the data to be processed becomes excessive for time-constrained (and, perhaps, realtime) database application environments. For example, for Scientific database applications, data gathering and analysis capabilities can be greatly enhanced if there are real- time/online querying capabilities that return approximate responses to check the quality of the generated scienctific data. Also, with real-time/online "on-the-fly" data analysis (querying) capabilities, the size of the stored data in scientific databases can be kept small and, thus, manageable. Similar problems occur in time-constrained manufacturing database applications. A new methodology is needed to cut down systematically the time to process the data involved in processing the query. To this end, one can use data samples and utilize statistical approximation to construct an approximate response to given query. In this research, a methodology will be developed for the construction of approximate query responses for databases under time (and/or accuracy) constraints, and with incomplete data.
