relational joins
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2021 ◽  
Vol 14 (7) ◽  
pp. 1228-1240
Author(s):  
Dimitrije Jankov ◽  
Binhang Yuan ◽  
Shangyu Luo ◽  
Chris Jermaine

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.


2017 ◽  
Vol 28 (9) ◽  
pp. 2663-2673 ◽  
Author(s):  
Makoto Yabuta ◽  
Anh Nguyen ◽  
Shinpei Kato ◽  
Masato Edahiro ◽  
Hideyuki Kawashima
Keyword(s):  

2017 ◽  
Vol 27 (5) ◽  
pp. 669-692 ◽  
Author(s):  
Darko Makreshanski ◽  
Georgios Giannikis ◽  
Gustavo Alonso ◽  
Donald Kossmann
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2013 ◽  
Vol 42 (4) ◽  
pp. 1737-1767 ◽  
Author(s):  
Albert Atserias ◽  
Martin Grohe ◽  
Dániel Marx
Keyword(s):  

Author(s):  
Bingsheng He ◽  
Ke Yang ◽  
Rui Fang ◽  
Mian Lu ◽  
Naga Govindaraju ◽  
...  

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