scholarly journals Size Bounds and Query Plans for Relational Joins

2013 ◽  
Vol 42 (4) ◽  
pp. 1737-1767 ◽  
Author(s):  
Albert Atserias ◽  
Martin Grohe ◽  
Dániel Marx
Keyword(s):  
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.


2001 ◽  
Vol 13 (1) ◽  
pp. 2-28 ◽  
Author(s):  
Ram D. Gopal ◽  
R. Ramesh ◽  
Stanley Zionts

1995 ◽  
Vol 7 (3) ◽  
pp. 257-268 ◽  
Author(s):  
Ram D. Gopal ◽  
R. Ramesh ◽  
Stanley Zionts

2017 ◽  
Vol 27 (5) ◽  
pp. 669-692 ◽  
Author(s):  
Darko Makreshanski ◽  
Georgios Giannikis ◽  
Gustavo Alonso ◽  
Donald Kossmann
Keyword(s):  

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

Sign in / Sign up

Export Citation Format

Share Document