GeOntoQuery - um mecanismo de busca em bancos de dados geográficos baseado em ontologias

Geographic Information System (GIS) are computational tools used to capture, store, consult, manipulate, analyze and print geo-referenced data. A GIS is a multi-disciplinary system that can be used by different communities of users, each one having their own interest and knowledge. This way, different knowledge views about the same reality need to be combined, in such way to attend each community. This work presents a mechanism that allows different community users access the same geographic database without knowing its particular internal structure. We use geographic ontologies to support a common and shared understanding of a specific domain: the coral reefs. Using these ontologies' descriptions that represent the knowledge of the different communities, mechanisms are created to handle with such different concepts. We use equivalent classes mapping, and a semantic layer that interacts with the ontologies and the geographic database, and that gives to the user the answers about his/her queries, independently of the used terms

Aplicação do método B ao projeto formal de software embarcado

This work shows a project method proposed to design and build software components from the software functional m del up to assembly code level in a rigorous fashion. This method is based on the B method, which was developed with support and interest of British Petroleum (BP). One goal of this methodology is to contribute to solve an important problem, known as The Verifying Compiler. Besides, this work describes a formal model of Z80 microcontroller and a real system of petroleum area. To achieve this goal, the formal model of Z80 was developed and documented, as it is one key component for the verification upto the assembly level. In order to improve the mentioned methodology, it was applied on a petroleum production test system, which is presented in this work. Part of this technique is performed manually. However, almost of these activities can be automated by a specific compiler. To build such compiler, the formal modelling of microcontroller and modelling of production test system should provide relevant knowledge and experiences to the design of a new compiler. In ummary, this work should improve the viability of one of the most stringent criteria for formal verification: speeding up the verification process, reducing design time and increasing the quality and reliability of the product of the final software. All these qualities are very important for systems that involve serious risks or in need of a high confidence, which is very common in the petroleum industry

Uma colônia de formigas para o caminho mais curto multiobjetivo

Multi-objective combinatorial optimization problems have peculiar characteristics that require optimization methods to adapt for this context. Since many of these problems are NP-Hard, the use of metaheuristics has grown over the last years. Particularly, many different approaches using Ant Colony Optimization (ACO) have been proposed. In this work, an ACO is proposed for the Multi-objective Shortest Path Problem, and is compared to two other optimizers found in the literature. A set of 18 instances from two distinct types of graphs are used, as well as a specific multiobjective performance assessment methodology. Initial experiments showed that the proposed algorithm is able to generate better approximation sets than the other optimizers for all instances. In the second part of this work, an experimental analysis is conducted, using several different multiobjective ACO proposals recently published and the same instances used in the first part. Results show each type of instance benefits a particular type of instance benefits a particular algorithmic approach. A new metaphor for the development of multiobjective ACOs is, then, proposed. Usually, ants share the same characteristics and only few works address multi-species approaches. This works proposes an approach where multi-species ants compete for food resources. Each specie has its own search strategy and different species do not access pheromone information of each other. As in nature, the successful ant populations are allowed to grow, whereas unsuccessful ones shrink. The approach introduced here shows to be able to inherit the behavior of strategies that are successful for different types of problems. Results of computational experiments are reported and show that the proposed approach is able to produce significantly better approximation sets than other methods