Understanding Quality in Conceptual Modeling
IEEE Software
Conceptual schema analysis: techniques and applications
ACM Transactions on Database Systems (TODS)
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A Methodology for Clustering Entity Relationship Models - A Human Information Processing Approach
ER '99 Proceedings of the 18th International Conference on Conceptual Modeling
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Conceptual Modeling of Information Systems
Conceptual Modeling of Information Systems
How to tame a very large ER diagram (using link analysis and force-directed drawing algorithms)
ER'05 Proceedings of the 24th international conference on Conceptual Modeling
A method for filtering large conceptual schemas
ER'10 Proceedings of the 29th international conference on Conceptual modeling
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The visualization and the understanding of large conceptual schemas require the use of specific methods. These methods generate clustered, summarized or focused schemas that are easier to visualize and to understand. All of these methods require computing the importance of each entity type in the schema. In principle, the totality of knowledge defined in the schema could be relevant for the computation of that importance but, up to now, only a small part of that knowledge has been taken into account. In this paper, we extend six existing methods for computing the importance of entity types by taking into account all the relevant knowledge defined in the structural and behavioural parts of the schema. We experimentally evaluate the original and the extended versions of those methods with two large real-world schemas. We present the two main conclusions we have drawn from the experiments.