Structured systems analysis and design method (2nd ed.): application and context
Structured systems analysis and design method (2nd ed.): application and context
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Conceptual schemas with abstractions making flat conceptual schemas more comprehensible
Data & Knowledge Engineering
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
SUPER - Visual Interaction with an Object-Based ER Model
ER '92 Proceedings of the 11th International Conference on the Entity-Relationship Approach: Entity-Relationship Approach
A Methodology for Clustering Entity Relationship Models - A Human Information Processing Approach
ER '99 Proceedings of the 18th International Conference on Conceptual Modeling
Filtering search results using an optimal set of terms identified by an artificial neural network
Information Processing and Management: an International Journal
A method for filtering large conceptual schemas
ER'10 Proceedings of the 29th international conference on Conceptual modeling
Design science in information systems research
MIS Quarterly
A tool for filtering large conceptual schemas
ER'11 Proceedings of the 30th international conference on Advances in conceptual modeling: recent developments and new directions
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
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Human understanding of constraint expressions (also called schema rules) in large conceptual schemas is very difficult. This is due to the fact that the elements (entity types, attributes, relationship types) involved in an expression are defined in different places in the schema, which may be very distant from each other and embedded in an intricate web of irrelevant elements. The problem is insignificant when the conceptual schema is small, but very significant when it is large. In this paper we describe a novel method that, given a set of constraint expressions and a large conceptual schema, automatically filters the conceptual schema, obtaining a smaller one that contains the elements of interest for the understanding of the expressions. We also show the application of the method to the important case of understanding the specification of event types, whose constraint expressions consists of a set of pre and postconditions. We have evaluated the method by means of its application to a set of large conceptual schemas. The results show that the method is effective and efficient. We deal with conceptual schemas in UML/OCL, but the method can be adapted to other languages.