The DOI matches its Crossref record.
A relational model of data for large shared data banks
Abstract
Future users of large data banks must be protected from having to know how the data is organized in the machine (the internal representation). A prompting service which supplies such information is not a satisfactory solution. Activities of users at terminals and most application programs should remain unaffected when the internal representation of data is changed and even when some aspects of the external representation are changed. Changes in data representation will often be needed as a result of changes in query, update, and report traffic and natural growth in the types of stored information. Existing noninferential, formatted data systems provide users with tree-structured files or slightly more general network models of the data. In Section 1, inadequacies of these models are discussed. A model based on n -ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are introduced. In Section 2, certain operations on relations (other than logical inference) are discussed and applied to the problems of redundancy and consistency in the user's model.
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
No references could be resolved yet.
Research works printed but not linked: 9
- CHILDS D.L., 1968, Proc. IFIP Cong., 162
- 10.1145/363790.363817 · DOI
- BACHMAN C. W., 1965, Software for random access processing. Dalamation (Apr
- GE, 1969, Annual Re. view in Automatic Programming 5, 13, 77
- 1968, Application Description Manual H20-052A-1
- GIS, 1965, Application Description Manual H20-0574
- 10.1145/800196.805973 · DOI
- CHURCH A., 1956, An Introduction to Mathematical Logic I
- FELDMAN J. A., 1968, Stanford Artificial Intelligence Rep. AI-66
Cited by
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
Natural language interfaces to databases – an introduction
Androutsopoulos, I., Ritchie, G. D., & Thanisch, P. (1995) · Natural Language Engineering · doi:10.1017/s135132490000005x · Cited by 768
Introduction: What is a knowledge graph?
Fensel, D., Şimşek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., Toma, I., Umbrich, J., & Wahler, A. (2020) · Knowledge Graphs · doi:10.1007/978-3-030-37439-6_1 · Cited by 244
On the representation and embedding of knowledge bases beyond binary relations
Wen, J., Li, J., Mao, Y., Chen, S., & Zhang, R. (2016) · arXiv · doi:10.48550/arxiv.1604.08642 · Cited by 91
Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database
Matallah, H., Belalem, G., & Bouamrane, K. (2021) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2021070104 · Cited by 46
Silberschatz, A., Korth, H. F., & Sudarshan, S. (1996) · ACM Computing Surveys · doi:10.1145/234313.234360 · Cited by 35
A Survey of Data Quality Measurement and Monitoring Tools
Ehrlinger, L., & Wöß, W. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.850611 · Cited by 25
SynWiki: Functional annotation of the first artificial organism Mycoplasma mycoidesJCVI‐syn3A
Pedreira, T., Elfmann, C., Singh, N., & Stülke, J. (2022) · Protein Science · doi:10.1002/pro.4179 · Cited by 13
Gubanov, M., Pyayt, A., & Karolak, A. (2024) · Proceedings of the 33rd ACM International Conference on Information and Knowledge Management · doi:10.1145/3627673.3680094 · Cited by 10
CASCADE: A Custom-Made Archiving System for the Conservation of Ancient DNA Experimental Data
Dolle, D., Fages, A., Mata, X., Schiavinato, S., Tonasso-Calvière, L., Chauvey, L., Wagner, S., Der Sarkissian, C., Fromentier, A., Seguin-Orlando, A., et al. (2020) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2020.00185 · Cited by 8
Metadata
- Authors
- Codd, E. F.
- Venue
- Communications of the ACM
- Published
- 1983-01
- Volume
- 26
- Issue
- 1
- Pages
- 64-69
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2165467455
- Cited by
- 4,796
- References
- 0
- FWCI
- 218.42
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.