A formal model for some behavioural features of analysis patterns

It is commonly said that a pattern has four essential parts: a statement of the context where the pattern is useful, the problem that the pattern addresses, the forces that play in forming a solution, and the solution that resolves that forces. This form underlies many published patterns, including analysis patterns. They show a number of highly generic processes that cut across traditional boundaries of system development and business engineering. However, patterns are invariably described informally in the literature, generally using natural language together some sort of graphical notation. A formal model of the semantic statements of analysis patterns has been proposed by reusing some of the properties formalised for GoF patterns. In this paper, we present a formal model of some behavioural properties of analysis patterns, and we illustrate using an example how an instantiation can be done. We also briefly discuss future work which will extend the model to include more behavioural propertiesÁrea: Ingeniería de Software - Bases de DatosRed de Universidades con Carreras en Informática (RedUNCI

Towards an Ontology-based Integration of Federated Information Sources

Integrating data from a Federated System is a very complex process that involves a series of tasks. Characteristics such as autonomy of the information sources, their geographical distribution and heterogeneity are some of the main problems we face to perform the integration. In this paper we focus on the problem of heterogeneity, more specifically on semantic heterogeneity. The semantic heterogeneity makes the integration difficult because of its bearing problems on synonymous, generalization/specialization, etc. Here, we briefly explain our three level approach to solve these problems. Then we show the structure of software components used to implement our supporting tool.Eje: Ingeniería en SoftwareRed de Universidades con Carreras en Informática (RedUNCI

A semi-automatic method for ontology mapping

Ontology mapping involves the task of finding similarities among overlapping sources by using ontologies. In a Federated System in which distributed, autonomous and heterogeneous information sources must be integrated, ontologies have emerged as tools to solve semantic heterogeneity problems. In this paper we propose a three-level approach that provides a semi-automatic method to ontology mapping. It performs some tasks automatically and guides the user in performing other tasks for which his/her intervention is required. Finally, a plug-in of the ontology editor, Protégé, is presented showing how the method is implemented through a case study.IV Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

A semi-automatic method for ontology mapping

Ontology mapping involves the task of finding similarities among overlapping sources by using ontologies. In a Federated System in which distributed, autonomous and heterogeneous information sources must be integrated, ontologies have emerged as tools to solve semantic heterogeneity problems. In this paper we propose a three-level approach that provides a semi-automatic method to ontology mapping. It performs some tasks automatically and guides the user in performing other tasks for which his/her intervention is required. Finally, a plug-in of the ontology editor, Protégé, is presented showing how the method is implemented through a case study.IV Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

An ontology approach to data integration

The term Federated Databases refers to the data integration of distributed, autonomous and heterogeneous databases. However, a federation can also include information systems, not only databases. At integrating data, several issues must be addressed. Here, we focus on the problem of heterogeneity, more specifically on semantic heterogeneity that is, problems rela ted to semantically equivalent concepts or semantically related/unrelated concepts. In order to address this problem, we apply the idea of ontologies as a tool for data integration. In this paper, we explain this concept and we briefly describe a method for constructing an ontology by using a hybrid ontology approach.Facultad de Informátic

An ontological approach to federated data integration

During the last years a lot of projects and lines of research have emerged from different proposals trying to find the best way to reach data integration. Two powerful techniques have appeared separately – ontology and contextual information – in order to help solve semantic heterogeneity problems. In our proposal we combine both techniques exploiting the advantages of each of them. We propose a new approach, in which three main components work together in order to achieve a consistent integration. Each component contains some type of semantic information modeled by ontologies and contexts. Our approach helps the building of each of the components and address other types of heterogeneity such as ontological heterogeneity.Eje: Ingeniería de Software y Bases de Datos (ISBD)Red de Universidades con Carreras en Informática (RedUNCI

An ontological approach to federated data integration

During the last years a lot of projects and lines of research have emerged from different proposals trying to find the best way to reach data integration. Two powerful techniques have appeared separately – ontology and contextual information – in order to help solve semantic heterogeneity problems. In our proposal we combine both techniques exploiting the advantages of each of them. We propose a new approach, in which three main components work together in order to achieve a consistent integration. Each component contains some type of semantic information modeled by ontologies and contexts. Our approach helps the building of each of the components and address other types of heterogeneity such as ontological heterogeneity.Eje: Ingeniería de Software y Bases de Datos (ISBD)Red de Universidades con Carreras en Informática (RedUNCI

Applying an ontology on data integration

The term “Federated Databases” refers to the data integration of distributed, autonomous and heterogeneous databases. However, a federation can also include information systems, not only databases. When integrating data, several issues must be addressed. Here, we focus on the problem of heterogeneity, more specifically on semantic heterogeneity – that is problems related to semantically equivalent concepts or semantically related/unrelated concepts. In order to address this problem, we apply the idea of ontologies as a tool for data integration. In this paper, we explain this concept and we briefly describe a method for constructing an ontology by using a hybrid ontology approach.Eje: Bases de datosRed de Universidades con Carreras en Informática (RedUNCI

Taking advantages of ontology and contexts to determine similarity of data

Data integration is the process of unifying data sharing some common semantics but are originated from unrelated sources. In our work we consider these sources are autonomous, heterogeneous and they are physically distributed. These three characteristics make the integration task more difficult as there are several aspects to bear in mind. In this work we only focus on one of these aspects, the semantic heterogeneity, which deals with the meaning of the concepts within the information sources. As each source contains a specific vocabulary according to its understanding of the world, terms denoting same meaning can be very difficult to find. In this paper we will briefly explain our method to find similarities using ontologies and contexts. We will propose some improvements in the similarity functions in order to take advantages of the information the ontologies provide.Eje: I - Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

Software Product Line Reengineering: A Case Study on the Geographic Domain

The growing adoption of software product lines (SPL) represents perhaps a paradigm shift in software development aiming at improving cost, quality, time to market, and developer productivity. While the underlying concepts are straightforward enough building a family of related products or systems by planned and careful reuse of a base of generalized software development assets the problems can be in the details, as successful product line practice involves domain understanding, technology selection, and so forth. Today, there is an important increment on reporting experiences and lessons about SPL development by capturing aspects that have been gathered during daily practice. Following this line, in this paper we start from our experiences of developing a software product line on the Marine Ecology domain highlighting our reasons for reengineering a previous SPL. Then, we explain step-bystep reengineering activities in terms of motivation, solutions, and lessons learned, which summarize strengths and limitations of the applied practices. Differently from other cases, here we take advantage of using domain standards as well as open source implementations within the geographic domain.Facultad de Informátic