Graduate Thesis Or Dissertation
 

Learning to Estimate Multi-Relation Aggregation Functions

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/6969z7769

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  • Multi-relation aggregation queries process the join operator before computing the aggregation function. This join is arguably the most costly operation since traditional join algorithms spend majority of their time trying to join the parts of the relations that do not generate any output tuples. This causes slow response times with large datasets in interactive exploratory environments. In this paper, we outline our vision on using online learning and adaptation to execute binary joins efficiently and to use the samples obtained from our join algorithm to estimate the aggregation functions. In our approach, the scan operators learn which parts of the relations are more likely to join during the query execution and estimate the average with confidence intervals as the joins are produced. Our empirical studies indicate that this approach outperforms current join algorithms and gives faster aggregate estimates.
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