The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Query evaluation techniques for large databases
Open accessAbstract
Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate the problem: In order to manipulate large sets of complex objects as efficiently as today's database systems manipulate simple records, query-processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and postrelational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set-matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The entity-relationship model—toward a unified view of data
Chen, P. P.-S. (1976) · ACM Transactions on Database Systems · doi:10.1145/320434.320440 · Cited by 4,708
Cited by
A comparison of join algorithms for log processing in MaPreduce
Blanas, S., Patel, J. M., Ercegovac, V., Rao, J., Shekita, E. J., & Tian, Y. (2010) · Proceedings of the 2010 ACM SIGMOD International Conference on Management of data · doi:10.1145/1807167.1807273 · Cited by 414
Query evaluation: Strategies and optimizations
Turtle, H., & Flood, J. (1995) · Information Processing & Management · doi:10.1016/0306-4573(95)00020-h · Cited by 305
Querying Datasets on the Web with High Availability
Verborgh, R., Hartig, O., De Meester, B., Haesendonck, G., De Vocht, L., Vander Sande, M., Cyganiak, R., Colpaert, P., Mannens, E., & Van de Walle, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-11964-9_12 · Cited by 85
Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective
Wadlom, N., Shen, J., & Lu, Y. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3802046
Metadata
- Authors
- Graefe, G.
- Venue
- ACM Computing Surveys
- Published
- 1993-06
- Volume
- 25
- Issue
- 2
- Pages
- 73-169
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2158237121
- Cited by
- 1,262
- References
- 1
- FWCI
- 40.40
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Query evaluation techniques for large databases. https://app.cochat.ai/papers/10.1145/152610.152611
- The paper
- Graefe, G. (1993). Query evaluation techniques for large databases. ACM Computing Surveys. https://doi.org/10.1145/152610.152611
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.