Processing aggregate relational queries with hard time constraints
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Selectivity and cost estimation for joins based on random sampling
Journal of Computer and System Sciences
Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Supporting time-constrained SQL queries in oracle
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
An experimental study of time-constrained aggregate queries
Proceedings of the 13th International Conference on Extending Database Technology
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The concept of time-constrained SQL queries was introduced to address the problem of long-running SQL queries. A key approach adopted for supporting time-constrained SQL queries is to use sampling to reduce the amount of data that needs to be processed, thereby allowing completion of the query in the specified time constraint. However, sampling does make the query results approximate and hence requires the system to estimate the values of the expressions (especially aggregates) occurring in the select list. Thus, coming up with estimates for aggregates is crucial for time-constrained approximate SQL queries to be useful, which is the focus of this paper. Specifically, we address the problem of estimating commonly occurring aggregates (namely, SUM, COUNT, AVG, MEDIAN, MIN, and MAX) in time-constrained approximate queries. We give both point and interval estimates for SUM, COUNT, AVG, and MEDIAN using Bernoulli sampling for various type of queries, including join processing with cross product sampling. For MIN (MAX), we give the confidence level that the proportion 100γ% of the population will exceed the MIN (or be less than the MAX) obtained from the sampled data.