View and index selection for query-performance improvement

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

View and index selection for query-performance improvement: quality-centered algorithms and heuristics

Full text

Pdf (354 KB)

Source

Conference on Information and Knowledge Management archive
Proceeding of the 17th ACM conference on Information and knowledge management table of contents

Napa Valley, California, USA

POSTER SESSION: Poster session 1 database table of contents

Pages 1329-1330

Year of Publication: 2008

ISBN:978-1-59593-991-3

Authors

Maxim Kormilitsin	NC State University, Raleigh, NC, USA
Rada Chirkova	NC State University, Raleigh, NC, USA
Yahya Fathi	NC State University, Raleigh, NC, USA
Matthias Stallmann	NC State University, Raleigh, NC, USA

Sponsors

ACM: Association for Computing Machinery
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 14, Downloads (12 Months): 85, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1458082.1458261 What is a DOI?

ABSTRACT

Selecting and precomputing indexes and materialized views, with the goal of improving query-processing performance, is an important part of database-performance tuning. The significant complexity of the view- and index-selection problem may result in high total cost of ownership for database systems. In this paper, we develop efficient methods that deliver user-specified quality of the set of selected views and indexes when given view- and index-based plans as problem inputs. Here, quality means proximity to the globally optimum performance for the input query workload given the input query plans. Our experimental results and comparisons on synthetic and benchmark instances demonstrate the competitiveness of our approach and show that it provides a winning combination with end-to-end view- and index-selection frameworks such as those of [1, 2].

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	Sanjay Agrawal , Surajit Chaudhuri , Vivek R. Narasayya, Automated Selection of Materialized Views and Indexes in SQL Databases, Proceedings of the 26th International Conference on Very Large Data Bases, p.496-505, September 10-14, 2000
	2	Nicolas Bruno , Surajit Chaudhuri, Automatic physical database tuning: a relaxation-based approach, Proceedings of the 2005 ACM SIGMOD international conference on Management of data, June 14-16, 2005, Baltimore, Maryland [doi>10.1145/1066157.1066184]
	3	Surajit Chaudhuri , Mayur Datar , Vivek Narasayya, Index Selection for Databases: A Hardness Study and a Principled Heuristic Solution, IEEE Transactions on Knowledge and Data Engineering, v.16 n.11, p.1313-1323, November 2004 [doi>10.1109/TKDE.2004.75]
	4	M. Held and R. M. Karp. The traveling salesman problem and minimum spanning trees. Oper. Research, 18:1138--1162, 1970.
	5	M. Kormilitsin, R. Chirkova, Y. Fathi, and M. Stallmann. Plan-based view and index selection for query-performance improvement. Technical report, NCSU, 2008. Available at http://www.csc.ncsu.edu/research/tech/reports.php.