Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
Oliver Kennedy , Department of Computer Science, Cornell University, Ithaca, NY, USA
Christoph Koch , Department of Computer Science, Cornell University, Ithaca, NY, USA
Estimation via sampling out of highly selective join queries is well known to be problematic, most notably in online aggregation. Without goal-directed sampling strategies, samples falling outside of the selection constraints lower estimation efficiency at best, and cause inaccurate estimates at worst. This problem appears in general probabilistic database systems, where query processing is tightly coupled with sampling. By committing to a set of samples before evaluating the query, the engine wastes effort on samples that will be discarded, query processing that may need to be repeated, or unnecessarily large numbers of samples.
Oliver Kennedy, Christoph Koch, "PIP: A database system for great and small expectations", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 157-168, doi:10.1109/ICDE.2010.5447879