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Award ID contains: 1910803

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  1. ; ; ; (, ACM SIGMOD Conference 2021)
    null (Ed.)
    Full Text Available 
  2. ; ; ; (, Proceedings of the VLDB Endowment)
    null (Ed.)
    When numerical and machine learning (ML) computations are expressed relationally, classical query execution strategies (hash-based joins and aggregations) can do a poor job distributing the computation. In this paper, we propose a two-phase execution strategy for numerical computations that are expressed relationally, as aggregated join trees (that is, expressed as a series of relational joins followed by an aggregation). In a pilot run, lineage information is collected; this lineage is used to optimally plan the computation at the level of individual records. Then, the computation is actually executed. We show experimentally that a relational system making use of this two-phase strategy can be an excellent platform for distributed ML computations. 
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  3. ; ; ; ; ; (, Proceedings of the VLDB Endowment)
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. (, SIGMOD Conference 2020)
    Full Text Available 
  5. (, Proceedings of the VLDB Endowment)
    Full Text Available