On computing the importance of entity types in large conceptual schemas

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.Peer ReviewedPostprint (published version

Extending the methods for computing the importance of entity types in large conceptual schemas

Visualizing and understanding large conceptual schemas requires the use of specific methods. These methods generate clustered, summarized, or focused schemas that are easier to visualize and 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 seven existing methods for computing the importance of entity types by taking into account more relevant knowledge defined in the structural and behavioural parts of the schema. We experimentally evaluate the original and extended versions of these methods with three large real-world schemas. We present the two main conclusions we have drawn from the experiments.Postprint (published version

Design and Implementation of a Conceptual Modeling Assistant (CMA)

This Master's Thesis de nes an architecture for a Conceptual Modeling Assistant (CMA) along with an implementation of a running prototype. Our CMA is a piece of software that runs on top of current modeling tools whose purpose is to collaborate with the conceptual modelers while developing a conceptual schema. The main functions of our CMA are to actively criticize the state of a conceptual schema, to suggest actions to do in order to improve the conceptual schema, and to o er new operations to automatize building a schema. On the one hand, the presented architecture assumes that the CMA has to be adapted to a modeling tool. Thus, the CMA permits the inclusion of new features, such as the detection of new defects to be criticized and new operations a modeler can execute, in a modeling tool. As a result, all modeling tools to which the CMA is adapted bene t of all these features without further work. On the other hand, the construction of our prototype involves three steps: the de nition of a simple, custom modeling tool; the implementation of the CMA; and the adaptation of the CMA to the custom modeling tool. Furthermore, we also present and implement some examples of new features that can be added to the CMA

Computing the Importance of Schema Elements Taking Into Account the Whole SCHEMA

Conceptual Schemas are one of the most important artifacts in the development cycle of information systems. To understand the conceptual schema is essential to get involved in the information system that is described within it. As the information system increases its size and complexity, the relative conceptual schema will grow in the same proportion making di cult to understand the main concepts of that schema/information system. The thesis comprises the investigation of the in uence of the whole schema in computing the relevance of schema elements. It will include research and implementation of algorithms for scoring elements in the literature, an study of the di erent results obtained once applied to a few example conceptual schemas, an extension of those algorithms including new components in the computation process like derivation rules, constraints and the behavioural subschema speci cation, and an in-depth comparison among the initial algorithms and the extended ones studying the results in order to choose those algorithms that give the most valuable output

On computing the importance of entity types in large conceptual schemas

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.Peer Reviewe

A filtering engine for large conceptual schemas

Postprint (published version

On computing the importance of entity types in large conceptual schemas

The visualization and the understanding of large conceptual schemas require the use of specific methods. These methods generateclustered, 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 thatimportance 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 partsof 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.Peer Reviewe

Design and Implementation of a Conceptual Modeling Assistant (CMA)

This Master's Thesis de nes an architecture for a Conceptual Modeling Assistant (CMA) along with an implementation of a running prototype. Our CMA is a piece of software that runs on top of current modeling tools whose purpose is to collaborate with the conceptual modelers while developing a conceptual schema. The main functions of our CMA are to actively criticize the state of a conceptual schema, to suggest actions to do in order to improve the conceptual schema, and to o er new operations to automatize building a schema. On the one hand, the presented architecture assumes that the CMA has to be adapted to a modeling tool. Thus, the CMA permits the inclusion of new features, such as the detection of new defects to be criticized and new operations a modeler can execute, in a modeling tool. As a result, all modeling tools to which the CMA is adapted bene t of all these features without further work. On the other hand, the construction of our prototype involves three steps: the de nition of a simple, custom modeling tool; the implementation of the CMA; and the adaptation of the CMA to the custom modeling tool. Furthermore, we also present and implement some examples of new features that can be added to the CMA

Computing the Importance of Schema Elements Taking Into Account the Whole SCHEMA

Conceptual Schemas are one of the most important artifacts in the development cycle of information systems. To understand the conceptual schema is essential to get involved in the information system that is described within it. As the information system increases its size and complexity, the relative conceptual schema will grow in the same proportion making di cult to understand the main concepts of that schema/information system. The thesis comprises the investigation of the in uence of the whole schema in computing the relevance of schema elements. It will include research and implementation of algorithms for scoring elements in the literature, an study of the di erent results obtained once applied to a few example conceptual schemas, an extension of those algorithms including new components in the computation process like derivation rules, constraints and the behavioural subschema speci cation, and an in-depth comparison among the initial algorithms and the extended ones studying the results in order to choose those algorithms that give the most valuable output