Currently, sampling algorithm has been incorporated into all major database systems. However, the estimate of accuracy obtained from the sample is unsolved.
This paper solve this problem by allowing GUS sampling operators to communicate with join and selection.
2) What is the solution’s main idea (nugget)?
It defines the notion of SOA equivalence. Second Order Analytical equivalence is the key equivalence relationship between query plans that is strong enough to allow quantile analysis but weak enough to ensure commutativity of sampling and relational operators.
Previous works mainly have two domains. 1)using sampling to derive estimates for single relation. 2) the extension of the correlated sampling pioneered by AQUA system.
They mainly analyze the sampling schemes uses functions over tuple not operators and algebras which the database is used to. Whereas the approach in this paper leverages operators and algebras.
4) Does the paper (or do you) identify any fundamental/hard trade-offs?
Even though it performs well for confidence level evaluation for sampling query process, the overhead of contracting this confidence evaluation process is high.
5) Do you think the paper will be influential in 10 years? Why or why not?
I think it will last for maybe 2-3 years. The computing overhead of confidence level is too large.
No comments:
Post a Comment