Paper Info
Title
Estimating aggregates in time-constrained approximate queries in Oracle
Abstract
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.
Year
DOI
Venue
2009
10.1145/1516360.1516487
EDBT
Keywords
Field
DocType
confidence level,long-running sql query,time-constrained sql query,time-constrained approximate query,query result,key approach,interval estimate,bernoulli sampling,time-constrained approximate sql,estimating aggregate,cross product sampling,interval estimation
SQL,Data mining,Population,Bernoulli sampling,Expression (mathematics),Cross product,Computer science,Oracle,Theoretical computer science,Sampling (statistics),Time constraint,Database
Conference
Citations 
PageRank 
References 
5
0.45
5
Authors
3
Name
Order
Citations
PageRank
Ying Hu131228.67
seema sundara21057.97
Jagannathan Srinivasan339744.67