Article

Free access

Maintaining views incrementally

Authors:

Inderpal Singh Mumick,

V. S. SubrahmanianAuthors Info & Claims

SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data

Pages 157 - 166

https://doi.org/10.1145/170035.170066

Published: 01 June 1993 Publication History

Abstract

We present incremental evaluation algorithms to compute changes to materialized views in relational and deductive database systems, in response to changes (insertions, deletions, and updates) to the relations. The view definitions can be in SQL or Datalog, and may use UNION, negation, aggregation (e.g. SUM, MIN), linear recursion, and general recursion.

We first present a counting algorithm that tracks the number of alternative derivations (counts) for each derived tuple in a view. The algorithm works with both set and duplicate semantics. We present the algorithm for nonrecursive views (with negation and aggregation), and show that the count for a tuple can be computed at little or no cost above the cost of deriving the tuple. The algorithm is optimal in that it computes exactly those view tuples that are inserted or deleted. Note that we store only the number of derivations, not the derivations themselves.

We then present the Delete and Rederive algorithm, DRed, for incremental maintenance of recursive views (negation and aggregation are permitted). The algorithm works by first deleting a superset of the tuples that need to be deleted, and then rederiving some of them. The algorithm can also be used when the view definition is itself altered.

References

[1]

Krzysztof R. Apt, Howard A. Blair, and Adrian Walker. Towards a Theory o} Declarative Knowledge. In Foundations o} Deductive Databases and Logic Programming. Editor J. Minker, 1988 Morgan Kaufmann.

Digital Library

[2]

Peter O. Buneman and Eric K. Clemons. Efficiently Monitoring Relational Databases. In A C#I TODS, Vol 4, No. 3, 1979, 368-382.

Digital Library

[3]

J.A. Blakeley, N. Coburn, and P. Larson. Updating Derived Relations: Detecting Irrelevant and Autonomously Computable Updates. In A CM TODS Vol 14, No. 3, 369-400, 1989.

Digital Library

[4]

Veronique Benzaken, Christophe Lecluse, and Philippe Richard. Enforcing Integrity Constraints in Database Programming Languages. TR Altair 68-91, Altair, France, 1991.

[5]

J.A. Blakeley, P. Larson, and F. W. Tompa. Ej#- c#ently Updating Materiahzed V#ews. In SIGMOD 1986, pages 61-71.

Digital Library

[6]

F. Bry, R. Manthey, and B. Martens. Integrity Verification in Knowledge Bases. In Logic Programm#ng, LNAI #9#, pages 114-189, 1992.

Digital Library

[7]

J.A. Blakeley and F. W. Tompa. Maintaining Materialized Views without Accessing Base Data. Information Systems, 13(4):393-406, 1988.

Digital Library

[8]

Stefano Ceri and Jennifer Widom. Deriving Production Rules for Incremental View Maintenance. In 17th VLDB, 1991.

Digital Library

[9]

Stefano Ceri and Jennifer Widom. Deriving Incremental Production Rules for Deductive Data. IBM RJ 9071, IBM Almaden, 1992.

[10]

S. Dar, R. Agrawal, and H. V. Jagadish. Optimization of generalized transitive closure, in Seventh IEEE International Con}erence on Data Engineering, Kobe, Japan, 1991.

Digital Library

[11]

Guozhu Dong and Jianwen Su. Incremental and Decremental Evaluation of Transitive Closure by First-Order Queries. TRCS 92-18, University of California, Santa Barbara, 1992.

[12]

Guozhu Dong and Rodney Topor. Incremental Evaluation o} Datalog Queries. In ICDT, 1992.

Digital Library

[13]

Ashish Gupta, Dinesh Katiyar, and Inderpal Singh Mumick. Counting Solutions to the View Maintenance Problem. In Workshop on Deductive Databases, JICSLP, 1992.

[14]

Ashish Gupta, Inderpal Singh Mumick, and V. S. Subrahmanian. Maintaining views incrementally. TR 921214-19-TM, AT&T Bell Labs, 1992.

[15]

John V. Harrison and Suzanne Dietrich. Maintenance of Materialized Views in a Deductive Database: An Update Propagation Approach. In Workshop on Deductive Databases, JICSLP, 1992.

[16]

ISO_ANSI. ISO-ANSI Working Draft: Database Language SQL2 and SQL3; X3H2; ISO/IEC JTC1/SC21/WG3, 1990.

[17]

V. Kuchenhoff. On the Efficient Computation of the Difference Between Consecutive Database States. In DOOD, LNCS 566, 1991.

[18]

Inderpal Singh Mumick and Oded Shmueli. Finiteness Properties of Database Queries. In Fourth Australian Database Conference, 1993.

[19]

Inderpal Singh Mumick. Query Optimization in Deductive and Relational Databases. Ph.D. Thesis, Stanford University, CA 94305, 1991.

[20]

J.M. Nicolas and Yazdanian. An Outline of BDGEN: A Deductive DBMS. In In}ormation Processing, pages 705-717, 1983.

[21]

Xiaolei Qian and Gio Wiederhold. Incremental Recomputation o} Active Relational Expressions. In A CM TKDE, 1991.

Digital Library

[22]

Torre Risch and Martin SkSld. Active rules based on object-oriented queries. To Appear, A CM TKDE, 1993.

[23]

Oded Shmueli and Alon Itai. Maintenance o} lQews. In Sigmod Record, 14(2):240-255, 1984.

Digital Library

[24]

Ulf Schreier, Hmnid Pirahesh, Rakesh Agrawal, and C. Mohan. Alert: An Architecture for Transforming a Passive DBMS Into an Active DBMS. In 17th VLDB, pages 469-478, 1991.

Digital Library

[25]

Jeffrey D. Ullman. Principles o} Database and Knowledge-Base Systems, Volumes I and 2. Computer Science Press, 1989.

Digital Library

[26]

Toni Urpi and Antoni Olive. A Method for Change Computation in Deductive Databases. In 18th VLDB, pages 225-237, 1992.

Digital Library

[27]

Allen Van Gelder. Negation as failure using tight derivations for general logic programs. In Third IEEE Symposium on Logic Programming, 1986. Springer-Verlag.

[28]

Ouri Wolfson, Hasanat M. Dewan, Salvatore J. Stolfo, and Yechiam Yemini. Incremental Evaluation of Rules and its Relationship to Parallelism. In SIGMOD 1991, pages 78-87.

Digital Library

Cited By

Nguyen Trong BTsushima KHu Z(2024)S_YNTHBX: An Example-guided Synthesizer for Bidirectional Programs on RelationsJournal of Information Processing10.2197/ipsjjip.32.47132(471-486)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.471
Trong BTsushima KHu Z(2024)Synthesis of Bidirectional Programs from Examples with Functional DependenciesJournal of Information Processing10.2197/ipsjjip.32.45132(451-465)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.451
Akidau T(2024)Simplicity and Elegance in Stream Processing: a 5-Year OdysseyProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3674954(4-5)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3629104.3674954
Show More Cited By

Index Terms

Maintaining views incrementally
1. Information systems
  1. Data management systems
    1. Database management system engines
    2. Query languages
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query languages (principles)

Recommendations

Maintaining views incrementally

We present incremental evaluation algorithms to compute changes to materialized views in relational and deductive database systems, in response to changes (insertions, deletions, and updates) to the relations. The view definitions can be in SQL or ...
Maintaining the Visibility Graph of a Dynamic Simple Polygon
Algorithms and Discrete Applied Mathematics
Abstract
We devise a fully-dynamic algorithm for maintaining the visibility graph of a given simple polygon P amid vertex insertions and deletions to the simple polygon. Our algorithm takes worst-case time to update the visibility graph when a vertex is ...
Maintaining views in object-relational databases

View materialization is an important way of improving the performance of query processing. When an update occurs to the source data from which a materialized view is derived, the materialized view has to be updated so that it is consistent with the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data

June 1993

566 pages

ISBN:0897915925

DOI:10.1145/170035

Editors:
Peter Buneman
Univ. of Pennsylvania
,
Sushil Jajodia

ACM SIGMOD Record Volume 22, Issue 2
June 1, 1993
558 pages
ISSN:0163-5808
DOI:10.1145/170036
Editors:
Peter Buneman
Univ. of Pennsylvania
,
Sushil Jajodia,
Won Kim
UniSQL, Inc., Austin, TX
Issue’s Table of Contents

Copyright © 1993 ACM.

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1993

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

SIGMOD/PODS93

Sponsor:

SIGMOD/PODS93: Joint ACM SIGMOD International Conference on Management of Data and ACM SIGMOD

May 25 - 28, 1993

D.C., Washington, USA

Acceptance Rates

Overall Acceptance Rate 785 of 4,003 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

596
Total Citations
View Citations
2,510
Total Downloads

Downloads (Last 12 months)342
Downloads (Last 6 weeks)60

Reflects downloads up to

Other Metrics

View Author Metrics

Citations

Cited By

Nguyen Trong BTsushima KHu Z(2024)S_YNTHBX: An Example-guided Synthesizer for Bidirectional Programs on RelationsJournal of Information Processing10.2197/ipsjjip.32.47132(471-486)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.471
Trong BTsushima KHu Z(2024)Synthesis of Bidirectional Programs from Examples with Functional DependenciesJournal of Information Processing10.2197/ipsjjip.32.45132(451-465)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.451
Akidau T(2024)Simplicity and Elegance in Stream Processing: a 5-Year OdysseyProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3674954(4-5)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3629104.3674954
Rong KBudiu MSkiadopoulos ASuresh LTai A(2023)Scaling a Declarative Cluster Manager Architecture with Query Optimization TechniquesProceedings of the VLDB Endowment10.14778/3603581.360359916:10(2618-2631)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.14778/3603581.3603599
Budiu MChajed TMcSherry FRyzhyk LTannen V(2023)DBSP: Automatic Incremental View Maintenance for Rich Query LanguagesProceedings of the VLDB Endowment10.14778/3587136.358713716:7(1601-1614)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.14778/3587136.3587137
Tang DFekete AGupta IParameswaran A(2023)Transactional Panorama: A Conceptual Framework for User Perception in Analytical Visual InterfacesProceedings of the VLDB Endowment10.14778/3583140.358316216:6(1494-1506)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.14778/3583140.3583162
Haeberlen APhan LMcGuire M(2023)Metaverse as a ServiceProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624662(298-307)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3620678.3624662
Szabó TChandra SBlincoe KTonella P(2023)Incrementalizing Production CodeQL AnalysesProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3613860(1716-1726)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3613860
Mohoney JPacaci AChowdhury SMousavi AIlyas IMinhas UPound JRekatsinas T(2023)High-Throughput Vector Similarity Search in Knowledge GraphsProceedings of the ACM on Management of Data10.1145/35897771:2(1-25)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589777
Akidau TBarbier PCseri IHueske FJones TLionheart SMills DPauliukevich DProbst LSemmler NSotolongo DZhang B(2023)What's the Difference? Incremental Processing with Change Queries in SnowflakeProceedings of the ACM on Management of Data10.1145/35897761:2(1-27)Online publication date: 20-Jun-2023
https://doi.org/10.1145/3589776
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

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

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents