Please use this identifier to cite or link to this item: https://doi.org/10.1145/1807167.1807238
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dc.titleContinuous sampling for online aggregation over multiple queries
dc.contributor.authorWu, S.
dc.contributor.authorOoi, B.C.
dc.contributor.authorTan, K.-L.
dc.date.accessioned2013-07-04T08:21:28Z
dc.date.available2013-07-04T08:21:28Z
dc.date.issued2010
dc.identifier.citationWu, S.,Ooi, B.C.,Tan, K.-L. (2010). Continuous sampling for online aggregation over multiple queries. Proceedings of the ACM SIGMOD International Conference on Management of Data : 651-662. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/1807167.1807238" target="_blank">https://doi.org/10.1145/1807167.1807238</a>
dc.identifier.isbn9781450300322
dc.identifier.issn07308078
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41180
dc.description.abstractIn this paper, we propose an online aggregation system called COSMOS (Continuous Sampling for Multiple queries in an Online aggregation System), to process multiple aggregate queries efficiently. In COSMOS, a dataset is first scrambled so that sequentially scanning the dataset gives rise to a stream of random samples for all queries. Moreover, COSMOS organizes queries into a dissemination graph to exploit the dependencies across queries. In this way, aggregates of queries closer to the root (source of data flow) can potentially be used to compute the aggregates of descendent/dependent queries. COSMOS applies some statistical approach to combine answers from ancestor nodes to generate the online aggregates for a node. COSMOS also offers a partitioning strategy to further salvage intermediate answers. We have implemented COSMOS and conducted an extensive experimental study in PostgreSQL. Our results on the TPC-H benchmark show the efficiency and effectiveness of COSMOS. © 2010 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1807167.1807238
dc.sourceScopus
dc.subjectdissemination graph
dc.subjectonline aggregation
dc.subjectrandom
dc.subjectsampling
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/1807167.1807238
dc.description.sourcetitleProceedings of the ACM SIGMOD International Conference on Management of Data
dc.description.page651-662
dc.identifier.isiutNOT_IN_WOS
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