ER model clustering as an aid for user communication and documentation in database design
Communications of the ACM
Conceptual schemas with abstractions making flat conceptual schemas more comprehensible
Data & Knowledge Engineering
Entity-relationship and object-oriented model automatic clustering
Data & Knowledge Engineering
Integrating information requirements along processes: a survey and research directions
ACM SIGSOFT Software Engineering Notes
Conceptual schema analysis: techniques and applications
ACM Transactions on Database Systems (TODS)
The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
A Rule-Based System Tool for Automated ER Model Clustering
Proceedings of the Eight International Conference on Enity-Relationship Approach to Database Design and Querying
Extending ER Model Clustering by Relationship Clustering
ER '93 Proceedings of the 12th International Conference on the Entity-Relationship Approach: Entity-Relationship Approach
Framework For Automatic Clustering of Semantic Models
ER '93 Proceedings of the 12th International Conference on the Entity-Relationship Approach: Entity-Relationship Approach
Abstraction Levels for Entity-Relationship Schemas
ER '94 Proceedings of the13th International Conference on the Entity-Relationship Approach
Benefits and Quality of Data Modelling - Results of an Empirical Analysis
ER '96 Proceedings of the 15th International Conference on Conceptual Modeling
A Methodology for Clustering Entity Relationship Models - A Human Information Processing Approach
ER '99 Proceedings of the 18th International Conference on Conceptual Modeling
Clustering Algorithms and Validity Measures
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Entity-Relationship modeling revisited
ACM SIGMOD Record
Defining and validating metrics for assessing the understandability of entity-relationship diagrams
Data & Knowledge Engineering
Enhancing Comprehension of Ontologies and Conceptual Models Through Abstractions
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Abstract ERIA: a web language for conceptual metadata integration and abstraction in the large
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
MEDI'12 Proceedings of the 2nd international conference on Model and Data Engineering
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Entity-relationship (ER) modeling is a widely accepted technique for conceptual database design. However, the complexities inherent in large ER diagrams have restricted the effectiveness of their use in practice. It is often difficult for end-users, or even for well-trained database engineers and designers, to fully understand and properly manage large ER diagrams. Hence, to improve their understandability and manageability, large ER diagrams need to be decomposed into smaller modules by clustering closely related entities and relationships. Previous researchers have proposed many manual and semi-automatic approaches for such clustering. However, most of them call for intuitive and subjective judgment from ''experts'' at various stages of their implementation. We present a fully automated algorithm that eliminates the need for subjective human judgment. In addition to improving their understandability and manageability, an automated algorithm facilitates the re-clustering of ER diagrams as they undergo many changes during their design, development, and maintenance phases. The validation methodology used in this study considers a set of both objective and subjective criteria for comparison. We adopted several concepts and metrics from machine-part clustering in cellular manufacturing (CM) while exploiting some of the characteristics of ER diagrams that are different from typical CM situations. Our algorithm uses well established criteria for good ER clustering solutions. These criteria were also validated by a group of expert database engineers and designers at NASA. An objective assessment of sample problems shows that our algorithm produces solutions with a higher degree of modularity and better goodness of fit compared with solutions produced by two commonly used alternative algorithms. A subjective assessment of sample problems by our expert database engineers and designers also found our solutions preferable to those produced by the two alternative algorithms.