Article

Free access

An overview of query optimization in relational systems

Author:

Surajit ChaudhuriAuthors Info & Claims

PODS '98: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems

Pages 34 - 43

https://doi.org/10.1145/275487.275492

Published: 01 May 1998 Publication History

PDF eReader

References

[1]

Apers, P.M.G., Hevner, A.R., Yao, S.B. Optimization Algorithms for Distributed Queries. IEEE Transactions on Software Engineering, Vol 9:1, 1983.

Google Scholar

[2]

Baneilhon, F., Maier, D., Sagiv, Y., Ullman, J.D. Magic sets and other strange ways to execute logic programs. In Proe. of ACM PODS, 1986.

Digital Library

Google Scholar

[3]

Bernstein, P.A., Goodman, N., Wong, E., Reeve, C.L, Rothnie, J. Query Processing in a System for Distributed Databases (SDD-I), ACM TODS 6:4 (Dee 1981).

Digital Library

Google Scholar

[4]

Chaudhuri, S., Shim K. An Overview of Cost-based Optimization of Queries with Aggregates. IEEE DIS Bulletin, Sep. 1995. (Special Issue on Query Processing).

Google Scholar

[5]

Chaudhuri, S., Shim K. Including Group-By in Query Optimization. In Proc. of VLDB, Santiago, 1994.

Digital Library

Google Scholar

[6]

Chaudhuri, S., Shim K. Query Optimization with aggregate views: In Proc. of EDBT, Avignon, 1996.

Digital Library

Google Scholar

[7]

Chaudhuri, S., Dayal, U. An Overview of Data Warehousing and OLAP Technology. In ACM SIGMOD Record, March 1997,

Digital Library

Google Scholar

[8]

Chaudhuri, S., Shim K. Optimization of Queries with Userdefined Predicates. In Proe. of VLDB, Murnbai, 1996.

Digital Library

Google Scholar

[9]

Chaudhuri, S., Krishnamurthy, R., Potamianos, S., Shim K, Optimizing Queries with Materialized Views. In Proe. of IEEE Data Engineering Conference, Taipei, 1995.

Digital Library

Google Scholar

[10]

Chaudhuri, S., Gravano, L. Optimizing Queries over Multimedia Repositories. In Proc. of ACM SIGMOD, Montreal, 1996.

Digital Library

Google Scholar

[11]

Chaudhuri, S., Motwani, R., Narasayya, V. Random Sampling for Histogram Construction: How much is enough? In Proe. of ACM SIGMOD, Seattle, 1998.

Digital Library

Google Scholar

[12]

Chimenti D., Gamboa R., Krishnamurthy R. Towards an Open Architecture for LDL. In Proe. of VLDB, Amsterdam, 1989.

Digital Library

Google Scholar

[13]

Dayal, U. Of Nests and Trees: A Unified Approach to Processing Queries That Contain Nested Subqueries, Aggregates and Quantifiers. In Proc. of VLDB, 1987.

Digital Library

Google Scholar

[14]

Fagin, R. Combining Fuzzy Information from Multiple Systems, In Proe. of ACM PODS, 1996.

Digital Library

Google Scholar

[15]

Finkelstein S., Common Expression Analysis in Database Applications. In Proe. of ACM SIGMOD, Orlando, 1982.

Digital Library

Google Scholar

[16]

Ganski, R.A., Long, H.K.T. Optimization of Nested SQL Queries Revisited. In Proe. of ACM SIGMOD, San Francisco, 1987.

Digital Library

Google Scholar

[17]

Gassner, P., Lohman, G., Sehiefer, K.B. Query Optimization in the IBM DB2 Family. IEF~ Data Engineering Bulletin, Dee. 1993.

Google Scholar

[18]

Gibbons, P.B., Matias, Y., Poosala, V. Fast Incremental Maintenance of Approximate Histograms. In Proe. of VLDB, Athens, 1997.

Digital Library

Google Scholar

[19]

Graefe, G., Ward K. Dynamic Query Evaluation Plans. In Proe. of ACM SIGMOD, Portland, 1989.

Digital Library

Google Scholar

[20]

Graefe G. Query Evaluation Techniques for Large Databases. In ACM Computing Surveys: Vo125, No 2., June 1993.

Digital Library

Google Scholar

[21]

Graefe, G. The Cascades Framework for Query Optimization. In Data Engineering Bulletin. Sept. 1995.

Google Scholar

[22]

Graefe, G., Dewitt DJ. The Exodus Optimizer Generator. In Proe. of ACM SIGMOD, San Francisco, 1987.

Digital Library

Google Scholar

[23]

Graefe, G, MeKenna, W.J. The Volcano Optimizer Generator: Extenslbility and Efficient Search. In Proe. of the IEEE Conference on Data Engineering, Vienna, 1993.

Digital Library

Google Scholar

[24]

Gray, J, Bosworth, A., Layman A., Pirahesh H. Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross- Tab, and Sub.Totals. In Proe. of IEEE Conference on Data Engineering, New Orleans, 1996.

Digital Library

Google Scholar

[25]

Gupta A, Harinarayan V., Quass D. Aggregate-query processing In data warehousing environments. In Proe. of VLDB, Zurich, 1995,

Digital Library

Google Scholar

[26]

Hnas, L, Freytag, J,C, Lohman, G.M., Pirahesh, H. Extensible Query Processing in Starburst. In Proe. of ACM SIGMOD, Portland, 1989.

Digital Library

Google Scholar

[27]

Haas, P,J, Naughton, J.F., $eshadri, S., Stokes, L. Sampling- Based Estimation of the Number of Distinct Values of an Attribute, In Proe, of VLDB, Zurich, 1995.

Digital Library

Google Scholar

[28]

Hasan, W, Optimization of SQL Queries for Parallel Machines. LNCS 1182, Springer, Verlag, 1996.

Digital Library

Google Scholar

[29]

Hellersteln J,M, Stonebraker, M. Predicate Migration: Optimization queries with expensive predicates. In Proe. of ACM SIGMOD, Washington D.C., 1993.

Digital Library

Google Scholar

[30]

Hellersteln, J.M, Predicate Migration placement. In Proe. of ACM SIGMOD, Minneapolis, 1994.

Google Scholar

[31]

Hong, W., Stonebraker, M. Optimization of Parallel Query Execution Plans in XPRS. In Proe. of Conference on Parallel and Distributed Information Systems. 1991.

Digital Library

Google Scholar

[32]

Hong, W, Parallel Query Processing Using Shared Memory Multlproeessors and Disk Arrays. Ph.D. Thesis, University of California, Berkeley, 1992.

Google Scholar

[33]

loannidis, Y, Ng, R.T, Shim, K., Sellis, T. Parametric Query Optimization. In Proe. of VLDB, Vancouver, 1992.

Digital Library

Google Scholar

[34]

loannldls, Y,E, Universality of Serial Histograms. In Proe. of VLDB, Dublin, ireland, 1993.

Digital Library

Google Scholar

[35]

Klm, W, On Optimizing an SQL-like Nested Query. ACM TODS, Vol 9, No, 3, 1982.

Digital Library

Google Scholar

[36]

Levy, A, Mumiek, I,S., $agiv, Y. Query Optimization by Predicate Move.Around, In Proe. of VLDB, Santiago, 1994.

Digital Library

Google Scholar

[37]

Lohman, G.M, Grammar-like Functional Rules for Representing Query Optimization Alternatives. In Proe. of ACM SIGMOD, 1988,

Digital Library

Google Scholar

[38]

Lohman, G, Mohan, C, Haas, L., Daniels, D., Lindsay, B., Selinger, P, Wilms, P. Query Processing in R*. In Query Processing in Database Systems. Springer Verlag, 1985.

Crossref

Google Scholar

[39]

Maekcrt, L,F, Lohman, G.M. R* Optimizer Validation and Performance Evaluation For Distributed Queries. In Readings in Database Systems, Morgan Kaufman.

Digital Library

Google Scholar

[40]

Maekert, L,F, Lohman, G.M. R* Optimizer Validation and Performance Evaluation for Local Queries. In Proe. of ACM SIGMOD, 1986,

Digital Library

Google Scholar

[41]

Melton, J, Simon A, Understanding The New SQL: A Complete Guide, Morgan Kaufman,

Digital Library

Google Scholar

[42]

Mumiek, I,S, Finkelstein, S., Pirahesh, H., Ramakrishnan, R. Magic is Relevant. In Proe. of ACM SIGMOD, Atlantic City, 1990,

Digital Library

Google Scholar

[43]

Mumick, I.S., Pimhesh, H. Implementation of Magic Sets in a Relational Database System. In Proe. of ACM SIGMOD, Montreal, 1994.

Digital Library

Google Scholar

[44]

Muralikrishna, M. Improved Unnesting Algorithms for Join Aggregate SQL Queries. In Pro(::. of VLDB, Vancouver, 1992.

Digital Library

Google Scholar

[45]

Muralikrishna M., Dewitt D.J. Equi-Depth Histograms for Estimating Selectivity Factors for Multi-Dimensional Queries, Proe. of ACM SIGMOD, Chicago, 1988.

Google Scholar

[46]

Ono, K., Lohman, G.M. Measuring the Complexity of Join Enumeration in Query Optimization. In Proe. of VLDB, Brisbane, 1990.

Digital Library

Google Scholar

[47]

Ozsu M.T., Valduriez, P. Principles of Distributed Database Systems. Prentice-Hall, 1991.

Digital Library

Google Scholar

[48]

Piatetsky-Shapiro, G., Connell, C. Accurate Estimation of the Number of Tuples Satisfying a Condition. In Proe. of ACM SIGMOD, 1984.

Digital Library

Google Scholar

[49]

Pirahesh, H., Hellerstein J.M., Hasan, W. F.xtensible/Rule Based Query Rewrite Optimization in Starburst. In Proe. of ACM SIGMOD 1992.

Digital Library

Google Scholar

[50]

Poosala, V., loannidis, Y., Haas, P., Shekita, E. Improved Histograms for Selectivity Estimation. In Proc. of ACM SIGMOD, Montreal, Canada 1996.

Digital Library

Google Scholar

[51]

Poosala, V., Ioannidis, Y.E. Selectivity Estimation Without the Attribute Value Independence Assumption. In Proe. of VLDB, Athens, 1997.

Digital Library

Google Scholar

[52]

Poosala, V., loannidis, Y.E., Haas, PJ., Shekita, E.J. Improved Histograms for Selectivity Estimation of Range Predicates In Proe. of ACM SIGMOD, Montreal, 1996.

Digital Library

Google Scholar

[53]

Rosenthal, A., Galindo-Legaria, C. Query Graphs, Implementing Trees, and Freely Reorderable Outerjoins. In Proe. of ACM SIGMOD, Atlantic City, 1990.

Digital Library

Google Scholar

[54]

Schneider, D.A. Complex Query Processing in Multiprocessor Database Machines. Ph.D. thesis, University of Wisconsin, Madison, Sept. 1990. Computer Sciences Teehaieal Report 965.

Digital Library

Google Scholar

[55]

Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price T.G. Access Path Selection in a Relational Database System. In Readings in Database Systems. Morgan Kaufman.

Digital Library

Google Scholar

[56]

Seshadri P., et al. Cost Based Optimization for Magic: Algebra and Implementation. In Proe. of ACM SIGMOD, Montreal, 1996.

Digital Library

Google Scholar

[57]

Seshadri, P., Pirahesh, H., Leung, T.Y.C. Decorrelating complex queries. In Proe. of the IEEE International Conference on Data Engineering, 1996.

Crossref

Google Scholar

[58]

Simmen, D., Shekita E., Malkemus T. Fundamental Techniques for Order Optimization. In Proe. of ACM SIGMOD, Montreal, 1996.

Digital Library

Google Scholar

[59]

Srivastava D., Dar S., Jagadish H.V., Levy A.: Answering Queries with Aggregation Using Vie,vs. Proe. of VLDB, Mumbai, 1996.

Digital Library

Google Scholar

[60]

Yah, Y.P., Larson P.A. Eager aggregation and lazy aggregation. In Pro(::. of VLDB Conference, Zurich, 1995.

Digital Library

Google Scholar

[61]

Yang, H.Z., Larson P.A. Query Transformation for PSJ-Queries. In Proe. of VLDB, 1987.

Digital Library

Google Scholar

Cited By

View all

Wanjiru Bvan Bommel PHiemstra D(2024)Dynamic and Partial Grading of SQL QueriesJournal of Engineering Research and Sciences10.55708/js03080013:8(1-14)Online publication date: Aug-2024
https://doi.org/10.55708/js0308001
Paduroiu AWi SYan YBurd RFarchtchi RFumarola G(2024)Membrane - Safe and Performant Data Access Controls in Apache Spark in the Presence of Imperative CodeProceedings of the VLDB Endowment10.14778/3685800.368580817:12(3813-3826)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685808
Fejza AGenevès PLayaïda N(2024)Efficient Enumeration of Recursive Plans in Transformation-Based Query OptimizersProceedings of the VLDB Endowment10.14778/3681954.368198617:11(3095-3108)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681986
Show More Cited By

Index Terms

An overview of query optimization in relational systems

Recommendations

Query processing over object views of relational data

This paper presents an approach to object view management for relational databases. Such a view mechanism makes it possible for users to transparently work with data in a relational database as if it was stored in an object-oriented (OO) database. A ...
Query optimization in relational database systems
Query optimization in multidatabase systems
CASCON '92: Proceedings of the 1992 conference of the Centre for Advanced Studies on Collaborative research - Volume 2

A multidatabase system (MDBS) integrates information from autonomous local databases managed by heterogeneous database management systems (DBMS) in a distributed environment. For a query involving more than one database, global query optimization should ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

PODS '98: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems

May 1998

286 pages

ISBN:0897919963

DOI:10.1145/275487

Chairmen:
Alberto Mendelson
Univ. of Toronto
,
Jan Paredaens
Univ. of Antwerp

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 1998

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

SIGMOD/PODS98

Sponsor:

SIGMOD/PODS98: Special Interest Group on Management of Data

June 1 - 4, 1998

Washington, Seattle, USA

Acceptance Rates

PODS '98 Paper Acceptance Rate 28 of 119 submissions, 24%;

Overall Acceptance Rate 642 of 2,707 submissions, 24%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

330
Total Citations
View Citations
9,832
Total Downloads

Downloads (Last 12 months)1,023
Downloads (Last 6 weeks)231

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wanjiru Bvan Bommel PHiemstra D(2024)Dynamic and Partial Grading of SQL QueriesJournal of Engineering Research and Sciences10.55708/js03080013:8(1-14)Online publication date: Aug-2024
https://doi.org/10.55708/js0308001
Paduroiu AWi SYan YBurd RFarchtchi RFumarola G(2024)Membrane - Safe and Performant Data Access Controls in Apache Spark in the Presence of Imperative CodeProceedings of the VLDB Endowment10.14778/3685800.368580817:12(3813-3826)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685808
Fejza AGenevès PLayaïda N(2024)Efficient Enumeration of Recursive Plans in Transformation-Based Query OptimizersProceedings of the VLDB Endowment10.14778/3681954.368198617:11(3095-3108)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681986
Bača R(2024)Window Function Expression: Let the Self-Join EnterProceedings of the VLDB Endowment10.14778/3665844.366584817:9(2162-2174)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665848
Zhao YLi ZCong G(2024)A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB StudiesProceedings of the VLDB Endowment10.14778/3636218.363623517:4(823-835)Online publication date: 5-Mar-2024
https://doi.org/10.14778/3636218.3636235
Ma WWang SVenet AGu JSubramanian SLiu RYang YFriedman DSperber MStevens P(2024)F3: A Compiler for Feature EngineeringProceedings of the 2nd ACM SIGPLAN International Workshop on Functional Software Architecture10.1145/3677998.3678220(3-9)Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3677998.3678220
Heddes MNunes IGivargis TNicolau A(2024)Convolution and Cross-Correlation of Count Sketches Enables Fast Cardinality Estimation of Multi-Join QueriesProceedings of the ACM on Management of Data10.1145/36549322:3(1-26)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654932
Currim SSnodgrass RSuh Y(2024)Identifying the Root Causes of DBMS SuboptimalityACM Transactions on Database Systems10.1145/363642549:1(1-40)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3636425
Kuiter EHeß TSundermann CKrieter SThüm TSaake G(2024)How Easy is SAT-Based Analysis of a Feature Model?Proceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3634713.3634733(149-151)Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1145/3634713.3634733
Ba JRigger MRoychoudhury APaiva AAbreu RStorey M(2024)CERT: Finding Performance Issues in Database Systems Through the Lens of Cardinality EstimationProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639076(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639076
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Query processing over object views of relational data

Query optimization in relational database systems

Query optimization in multidatabase systems