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Title: Distributed numerical and machine learning computations via two-phase execution of aggregated join trees
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.  more » « less
Award ID(s):
1910803 2008240 1918651
PAR ID:
10280662
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the VLDB Endowment
Volume:
14
Issue:
7
ISSN:
2150-8097
Page Range / eLocation ID:
1228 to 1240
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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