The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index; 2 of the 2 sources its claims cite were checked for support.
Data models
Open accessAbstract
article Free Access Share on Data models Authors: Avi Silberschatz Bell Laboratories, Lucent Technologies Inc. Bell Laboratories, Lucent Technologies Inc.View Profile , Henry F. Korth Bell Laboratories, Lucent Technologies Inc. Bell Laboratories, Lucent Technologies Inc.View Profile , S. Sudarshan Indian Institue of Technology, Bombay Indian Institue of Technology, BombayView Profile Authors Info & Claims ACM Computing SurveysVolume 28Issue 1March 1996 pp 105–108https://doi.org/10.1145/234313.234360Published:01 March 1996Publication History 30citation1,670DownloadsMetricsTotal Citations30Total Downloads1,670Last 12 Months188Last 6 weeks47 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Overview
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Written by AI from the paper’s full text.
TL;DR
This article surveys database data models, classifying them into object-based, record-based, and physical categories while focusing on logical models. It describes the entity-relationship, object-oriented, relational, network, hierarchical, and object-relational approaches and notes a trend toward combining relational and object-oriented features.
Key findings
- Data models are grouped into three broad categories: object-based logical, record-based logical, and physical; the article sets physical models aside because they concern implementation details.
- Object-based models include the entity-relationship model, with entity sets, relationship sets, attributes, primary keys, and extensions such as specialization, generalization, and aggregation, and the object-oriented model, with encapsulation, instance variables, messages, methods, classes, and inheritance.
- Record-based models structure data as fixed-format records; the relational model is described as the leading commercial model, grounded in mathematical relations and tables, with relational calculus and algebra as formal query foundations and SQL-92 as the established standard.
- The network and hierarchical models are tied more closely to physical design and are declining in importance, while object-relational hybrids extend the relational model with complex types, object identity, and inheritance; SQL-3 was under development with object-oriented features.
Methods
- Conceptual survey and taxonomy rather than an empirical study; it organizes and compares data models by their descriptive primitives and semantic expressiveness.
- No dataset or sample is used; the discussion draws on prior database literature and texts, including work by Chen, Codd, Kim, Maier, and Silberschatz et al.
- Main analysis is descriptive: it explains modeling concepts, query languages, standards, and convergence trends across model families.
Limitations
- The paper states no limitations. Evident from the text: it offers no empirical evaluation, comparative metrics, or systematic review protocol.
- Evident from the text: coverage is brief and high-level, and physical data models are explicitly excluded from the discussion.
- Evident from the text: the object-relational discussion is intentionally short, and the 1996 publication date makes its standards and market observations historical rather than current.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
15 Own interpretation · 4 No basis given · 2 Supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, the model is tied more closely to physical-level design than is the relational model.”
§ Data Models > RECORD-BASED LOGICAL MODELS
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 19
- It may state two separate claims as one. 6
- The quote only partly states this claim. 1
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Citations
Cites
The Entity Relationship Model — Toward a Unified View of Data
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The entity-relationship model—toward a unified view of data
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Research works printed but not linked: 2
- MAIER D. 1983. The Theory of Relational Databases. Computer Science Press Rockville MD. MAIER D. 1983. The Theory of Relational Databases. Computer Science Press Rockville MD.
- SILBERSCHATZ A. KORTH H. F. AND SUDARSHAN S. 1996. Database System Concepts third edition. McGraw Hill New York. SILBERSCHATZ A. KORTH H. F. AND SUDARSHAN S. 1996. Database System Concepts third edition. McGraw Hill New York.
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A Comparison of Current Graph Database Models
Angles, R. (2012) · doi:10.1109/icdew.2012.31 · Cited by 262
“In the most general sense a data model is a collection of conceptual tools used to model representations of real-world entities and the relations among these entities [7].”
BackgroundDavoudian, A., Chen, L., & Liu, M. (2018) · ACM Computing Surveys · doi:10.1145/3158661 · Cited by 222
“A data model specifies how real-world entities and their relationships are represented and operated (Silberschatz et al. 1996).”
MethodQuerying RDF Data from a Graph Database Perspective
Angles, R., & Gutiérrez, C. (2005) · Lecture notes in computer science · doi:10.1007/11431053_24 · Cited by 147
“In a broad or abstract sense, a data model is a collection of conceptual tools for describing the real-world entities to be modeled in the database and the relationships among these entities [3].”
BackgroundModeling and Processing Information for Context-Aware Computing: A Survey
Hoareau, C., & Satoh, I. (2009) · New Generation Computing · doi:10.1007/s00354-009-0060-5 · Cited by 75
Allen, G. N., & March, S. T. (2006) · MIS Quarterly · doi:10.5555/2017307.2017312 · Cited by 41
Handling Big Data Using a Data-Aware HDFS and Evolutionary Clustering Technique
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“model representations of real-world entities and the relations among these entities [10].”
BackgroundAllen, G. N., & March, S. T. (2006) · MIS Quarterly · doi:10.2307/25148731 · Cited by 30
Noor, A. K. (1997) · Computers & Structures · doi:10.1016/s0045-7949(96)00369-0 · Cited by 28
Kadaster Knowledge Graph: Beyond the Fifth Star of Open Data
Ronzhin, S., Folmer, E., Maria, P., Brattinga, M., Beek, W., Lemmens, R. L. G., & Veer, R. V. '. (2019) · Information · doi:10.3390/info10100310 · Cited by 26
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Pierro, D. D., Ferilli, S., & Redavid, D. (2023) · Information · doi:10.3390/info14030154 · Cited by 29
Data Representations for WordNet: A Case for RDF
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Folmer, E., Ronzhin, S., van Hillegersberg, J., Beek, W., & Lemmens, R. L. G. (2020) · IEEE Access · doi:10.1109/access.2020.2984691 · Cited by 9
“This standard is based on mature technologies: the graph data model [13] and the Hypertext Transfer Protocol (HTTP) [14].”
MethodPlugging and Playing with Variety of Data using Multi-Model Database and Polyglot Persistence
Sachdeva, S., & Vasava, J. (2024) · doi:10.1109/iceeict61591.2024.10718481 · Cited by 7
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“sentation of real-world entities and the relationships among them [18].”
BackgroundProvenance Framework for Twitter Data using Zero-Information Loss Graph Database
Rani, A., Goyal, N., & Gadia, S. K. (2020) · doi:10.1145/3430984.3431014 · Cited by 5
“Modeling of twitter data has been proposed in [4] using Neo4j [11] for different purposes viz.”
MethodSemantic Property Graph for Scalable Knowledge Graph AnalyticsPreprint
Purohit, S., Van, N., & Chin, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.07410 · Cited by 5
“model representations of real-world entities and the relations among these entities [10].”
BackgroundA comparison of UML and ORM for data modeling
Bloesch, A. (1999) · Cited by 5
Research Frontiers in Object Technology
March, S. T., Wood, C. A., & Allen, G. N. (1999) · Information Systems Frontiers · doi:10.1023/a:1010016913052 · Cited by 4
Social data provenance framework based on zero-information loss graph database
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Griso, D. F., & Amengual, C. (2009) · RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya) · doi:10.5821/sth.v0i4.1062 · Cited by 2
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Aydın, A. A. (2025) · GAZI UNIVERSITY JOURNAL OF SCIENCE · doi:10.35378/gujs.1467890 · Cited by 2
“In [15], the “data model” was explained as “the structure of a database, a collection of conceptual tools for describing the real-world entities to be modeled in the database and the relationships among these entities.””
Big Data Addressed in Natural Language
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“Physical data models are used to describe data at the lowest level [ 5, 6 ].”
MethodStoll, O., Züst, S., Rodel, E., & West, S. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-43666-6_2
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Hampel, G. (2021) · Jelenkori Társadalmi és Gazdasági Folyamatok · doi:10.14232/jtgf.2021.3-4.57-64
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Hampel, G. (2020) · Jelenkori Társadalmi és Gazdasági Folyamatok · doi:10.14232/jtgf.2020.3-4.119-132
Castellanos, A. (2016) · doi:10.25148/etd.fidc000777
“…without a predefined data model (e.g., free-form text) and we include semi-structured data that is 56 neither raw (e.g., images, sound) nor explicitly structured (e.g., data in a relational database) in our definition (Zhu and Azar, 2015; Abiteboul, 1997; Silberschatz et al., 1996; Buneman, 1997).”
Prosessitietomallin toteutus graafitietokannassa
Tyni, N. (2015) · Tampere University Institutional Repository (Tampere University)
PSIS: Parallel Semantic Indexing System - Preliminary Experiments
Amato, F., Gargiulo, F., Moscato, V., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03889-6_15
“Graph Database models are conceptual tools used to represent the real-world entities and the relationship among them [7] but the term also represents structured types of data and mathematical frameworks .”
BackgroundIntegrating Heterogeneous Data by Extending Semantic Web Standards
Lopes, N. (2012) · Arrow@dit (Dublin Institute of Technology) · doi:10.13025/16523
Wang, G. C., Sheu, P. C. ‐., Kitazawa, A., et al. (2007) · Wiley Encyclopedia of Computer Science and Engineering · doi:10.1002/9780470050118.ecse098
Metadata
- Authors
- Silberschatz, A., Korth, H. F., & Sudarshan, S.
- Affiliations
- Bell Laboratories, Lucent Technologies Inc.
- Indian Institute of Technology Bombay (IN)
- Venue
- ACM Computing Surveys
- Published
- 1996-03
- Volume
- 28
- Issue
- 1
- Pages
- 105-108
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2295965385
- Semantic Scholar
- fb0152d404d28f91fb8539e9a90a6c0475e50ed5
- Cited by
- 35
- References
- 7
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 2 · 2020: 3 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 2
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Computing Surveys (publishedVersion)
- DSpace (IIT Bombay) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- data model, database model, data representation
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