Developing a GIS-Database and Risk Index for Potentially Polluting Marine Sites

The increasing availability of geospatial marine data provides an opportunity for hydrographic offices to contribute to the identification of “Potentially Polluting Marine Sites” (PPMS). These include shipwrecks, oil rigs, pipelines, and dumping areas. To adequately assess the environmental risk of these sites, relevant information must be collected and converted into a multi-scale geodatabase suitable for site inventory and geo-spatial analysis. In addition, a Risk Index – representing an assessment of the magnitude of risk associated with any site – can be derived to determine the potential impacts of these PPMS. However, the successful collection and integration of PPMS information requires some effort to ‘normalize’ and standardize the data based on recognized international standards. In particular, there is benefit in structuring the data in conformance with the Universal Hydrographic Data Model (IHO S-100) recently adopted by the International Hydrographic Organization. In this paper, an S-100 compliant product specification for a PPMS geo-spatial database and associated Marine Site Risk Index is proposed which can be used by national hydrographic offices and marine protection agencies

Ontology-based model abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models’ information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models

A filtering engine for large conceptual schemas

Postprint (published version

Understanding constraint expressions in large conceptual schemas by automatic filtering

Human understanding of constraint expressions (also called schema rules) in large conceptual schemas is very di cult. This is due to the fact that the elements (entity types, attributes, relationship types) involved in an expression are de ned in di fferent places in the schema, which may be very distant from each other and embedded in an intricate web of irrelevant elements. The problem is insignifi cant when the conceptual schema is small, but very signi cant 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 specication 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 eff ective and e cient. We deal with conceptual schemas in UML/OCL, but the method can be adapted to other languages.Peer ReviewedPreprin

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