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Optimizing inequality joins in Datalog with approximated constraint propagation

Dario Campagna, Beata Sarna-Starosta and Tom Schrijvers UGent (2012) Lecture Notes in Computer Science. 7149. p.108-122
abstract
Datalog systems evaluate joins over arithmetic (in)equalities as a naive generate-and-test of Cartesian products. We exploit aggregates in a source-to-source transformation to reduce the size of Cartesian products and to improve performance. Our approach approximates the well-known propagation technique from Constraint Programming. Experimental evaluation shows good run time speed-ups on a range of non-recursive as well as recursive programs. Furthermore, our technique improves upon the previously reported in the literature constraint magic set transformation approach.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
Logic Programming, Datalog, program transformation, LogicBlox, bounds consistency, Constraint Programming
in
Lecture Notes in Computer Science
Lect. Notes Comput. Sci.
editor
Claudio Russo and Neng-Fa Zhou
volume
7149
issue title
Practical aspects of declarative languages
pages
108 - 122
publisher
Springer
place of publication
Heidelberg, Germany
conference name
14th International symposium on Practical Aspects of Declarative Languages
conference location
Philadelphia, PA, USA
conference start
2012-01-23
conference end
2012-01-24
ISSN
0302-9743
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1940522
handle
http://hdl.handle.net/1854/LU-1940522
date created
2011-11-09 10:57:14
date last changed
2012-01-26 12:50:40
@inproceedings{1940522,
  abstract     = {Datalog systems evaluate joins over arithmetic (in)equalities as a naive generate-and-test of Cartesian products. We exploit aggregates in a source-to-source transformation to reduce the size of Cartesian products and to improve performance. Our approach approximates the well-known propagation technique from Constraint Programming.
Experimental evaluation shows good run time speed-ups on a range of non-recursive as well as recursive programs. Furthermore, our technique improves upon the previously reported in the literature constraint magic set transformation approach.},
  author       = {Campagna, Dario and Sarna-Starosta, Beata and Schrijvers, Tom},
  booktitle    = {Lecture Notes in Computer Science},
  editor       = {Russo, Claudio and Zhou, Neng-Fa},
  issn         = {0302-9743},
  keyword      = {Logic Programming,Datalog,program transformation,LogicBlox,bounds consistency,Constraint Programming},
  language     = {eng},
  location     = {Philadelphia, PA, USA},
  pages        = {108--122},
  publisher    = {Springer},
  title        = {Optimizing inequality joins in Datalog with approximated constraint propagation},
  volume       = {7149},
  year         = {2012},
}

Chicago
Campagna, Dario, Beata Sarna-Starosta, and Tom Schrijvers. 2012. “Optimizing Inequality Joins in Datalog with Approximated Constraint Propagation.” In Lecture Notes in Computer Science, ed. Claudio Russo and Neng-Fa Zhou, 7149:108–122. Heidelberg, Germany: Springer.
APA
Campagna, D., Sarna-Starosta, B., & Schrijvers, T. (2012). Optimizing inequality joins in Datalog with approximated constraint propagation. In C. Russo & N.-F. Zhou (Eds.), Lecture Notes in Computer Science (Vol. 7149, pp. 108–122). Presented at the 14th International symposium on Practical Aspects of Declarative Languages, Heidelberg, Germany: Springer.
Vancouver
1.
Campagna D, Sarna-Starosta B, Schrijvers T. Optimizing inequality joins in Datalog with approximated constraint propagation. In: Russo C, Zhou N-F, editors. Lecture Notes in Computer Science. Heidelberg, Germany: Springer; 2012. p. 108–22.
MLA
Campagna, Dario, Beata Sarna-Starosta, and Tom Schrijvers. “Optimizing Inequality Joins in Datalog with Approximated Constraint Propagation.” Lecture Notes in Computer Science. Ed. Claudio Russo & Neng-Fa Zhou. Vol. 7149. Heidelberg, Germany: Springer, 2012. 108–122. Print.