Please use this identifier to cite or link to this item: https://doi.org/10.1145/1807167.1807238
Title: Continuous sampling for online aggregation over multiple queries
Authors: Wu, S. 
Ooi, B.C. 
Tan, K.-L. 
Keywords: dissemination graph
online aggregation
random
sampling
Issue Date: 2010
Source: Wu, 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. https://doi.org/10.1145/1807167.1807238
Abstract: In 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.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
URI: http://scholarbank.nus.edu.sg/handle/10635/41180
ISBN: 9781450300322
ISSN: 07308078
DOI: 10.1145/1807167.1807238
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