ACM Home Page
Please provide us with feedback. Feedback
Query optimizers: time to rethink the contract?
Full text PdfPdf (539 KB)
Source
International Conference on Management of Data archive
Proceedings of the 35th SIGMOD international conference on Management of data table of contents
Providence, Rhode Island, USA
SESSION: Industrial session 6: industrial directions table of contents
Pages 961-968  
Year of Publication: 2009
ISBN:978-1-60558-551-2
Author
Surajit Chaudhuri  Microsoft Research, Redmond, WA, USA
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 83,   Downloads (12 Months): 320,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1559845.1559955
What is a DOI?

ABSTRACT

Query Optimization is expected to produce good execution plans for complex queries while taking relatively small optimization time. Moreover, it is expected to pick the execution plans with rather limited knowledge of data and without any additional input from the application. We argue that it is worth rethinking this prevalent model of the optimizer. Specifically, we discuss how the optimizer may benefit from leveraging rich usage data and from application input. We conclude with a call to action to further advance query optimization technology.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
Graefe, G.: The Cascades Framework for Query Optimization. IEEE Data Eng. Bull. 18(3): 19--29 (1995)
3
4
 
5
 
6
 
7
8
9
 
10
Zilberstein, S.: Using Anytime Algorithms in Intelligent Systems. AI Magazine 17(3): 73--83 (1996)
11
 
12
Mayrhofer, R.: Generic Heuristics for Combinatorial Optimization Problems. Proc. of the 9th International Conference on Operational Research 2002
 
13
 
14
 
15
 
16
 
17
 
18
19
20
21
22
23
24
 
25
 
26
 
27
 
28
29
30
 
31
32
 
33
34