Application of ontologies in peer-to-peer database with routing queries based on ant colony algorithm

A significant set of information stored in different databases around the world, can be shared through peer-topeer databases. With that, is obtained a large base of knowledge, without the need for large investments because they are used existing databases, as well as the infrastructure in place. However, the structural characteristics of peer-topeer, makes complex the process of finding such information. On the other side, these databases are often heterogeneous in their schemas, but semantically similar in their content. A good peer-to-peer databases systems should allow the user access information from databases scattered across the network and receive only the information really relate to your topic of interest. This paper proposes to use ontologies in peer-to-peer database queries to represent the semantics inherent to the data. The main contribution of this work is enable integration between heterogeneous databases, improve the performance of such queries and use the algorithm of optimization Ant Colony to solve the problem of locating information on peer-to-peer networks, which presents an improve of 18% in results. © 2011 IEEE.

Architecture for peer-to-peer databases with routing queries using ant colony algorithm and semantic support

In a peer-to-peer network, the nodes interact with each other by sharing resources, services and information. Many applications have been developed using such networks, being a class of such applications are peer-to-peer databases. The peer-to-peer databases systems allow the sharing of unstructured data, being able to integrate data from several sources, without the need of large investments, because they are used existing repositories. However, the high flexibility and dynamicity of networks the network, as well as the absence of a centralized management of information, becomes complex the process of locating information among various participants in the network. In this context, this paper presents original contributions by a proposed architecture for a routing system that uses the Ant Colony algorithm to optimize the search for desired information supported by ontologies to add semantics to shared data, enabling integration among heterogeneous databases and the while seeking to reduce the message traffic on the network without causing losses in the amount of responses, confirmed by the improve of 22.5% in this amount. © 2011 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

A modified ant colony optimization algorithm modeled on tabu-search methods

An earlier model underlying the foraging strategy of a pachycodyla apicalis ant is modified. The proposed algorithm incorporates key features of the tabu-search method in the development of a relatively simple but robust global ant colony optimization algorithm. Numerical results are reported to validate and demonstrate the feasibility and effectiveness of the proposed algorithm in solving electromagnetic (EM) design problems.

Optimization of query results in peer-to-peer database supported by semantics

The development of new technologies that use peer-to-peer networks grows every day, with the object to supply the need of sharing information, resources and services of databases around the world. Among them are the peer-to-peer databases that take advantage of peer-to-peer networks to manage distributed knowledge bases, allowing the sharing of information semantically related but syntactically heterogeneous. However, it is a challenge to ensure the efficient search for information without compromising the autonomy of each node and network flexibility, given the structural characteristics of these networks. On the other hand, some studies propose the use of ontology semantics by assigning standardized categorization of information. The main original contribution of this work is the approach of this problem with a proposal for optimization of queries supported by the Ant Colony algorithm and classification though ontologies. The results show that this strategy enables the semantic support to the searches in peer-to-peer databases, aiming to expand the results without compromising network performance. © 2011 IEEE.

Algoritmos de alinhamento múltiplo e técnicas de otimização para esses algoritmos utilizando Ant Colony

Pós-graduação em Ciência da Computação - IBILCE

Roteamento de consultas em banco de dados peer-to-peer utilizando colônias de formigas e ontologias

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An approach based on neural networks for identification of fault sections in radial distribution systems

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Characterization of operational safety in offshore oil wells

The main concern of activities developed in oil and gas well construction is safety. But safety during the well construction process is not a trivial subject. Today risk evaluation approaches are based in static analyses of existent systems. In other words, those approaches do not allow a dynamic analysis that evaluates the risk for each alteration of the context. This paper proposes the use of Quantitative and Dynamic Risk Assessment (QDRA) to assess the degree of safety of each planned job. The QDRA can be understood as a safe job analysis approach, developed with the purpose of quantifying the safety degree in entire well construction and maintenance activities. The QDRA is intended to be used in the planning stages of well construction and maintenance, where the effects of hazard on job sequence are important unknowns. This paper also presents definitions of barrier, and barriers integrated set (BIS), and a modeling technique showing their relationships. (c) 2006 Elsevier B.V. All rights reserved.

Fundamentos de otimização por inteligência de enxames: uma visão geral

Este artigo apresenta uma breve revisão de alguns dos mais recentes métodos bioinspirados baseados no comportamento de populações para o desenvolvimento de técnicas de solução de problemas. As metaheurísticas tratadas aqui correspondem às estratégias de otimização por colônia de formigas, otimização por enxame de partículas, algoritmo shuffled frog-leaping, coleta de alimentos por bactérias e colônia de abelhas. Os princípios biológicos que motivaram o desenvolvimento de cada uma dessas estratégias, assim como seus respectivos algoritmos computacionais, são introduzidos. Duas aplicações diferentes foram conduzidas para exemplificar o desempenho de tais algoritmos. A finalidade é enfatizar perspectivas de aplicação destas abordagens em diferentes problemas da área de engenharia.

Manipulation of fire ant density, Solenopsis spp., for short-term reduction of Diatraea saccharalis larval densities in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

A bayesian analysis for the parameters of the exponential-logarithmic distribution

The exponential-logarithmic is a new lifetime distribution with decreasing failure rate and interesting applications in the biological and engineering sciences. Thus, a Bayesian analysis of the parameters would be desirable. Bayesian estimation requires the selection of prior distributions for all parameters of the model. In this case, researchers usually seek to choose a prior that has little information on the parameters, allowing the data to be very informative relative to the prior information. Assuming some noninformative prior distributions, we present a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Jeffreys prior is derived for the parameters of exponential-logarithmic distribution and compared with other common priors such as beta, gamma, and uniform distributions. In this article, we show through a simulation study that the maximum likelihood estimate may not exist except under restrictive conditions. In addition, the posterior density is sometimes bimodal when an improper prior density is used. © 2013 Copyright Taylor and Francis Group, LLC.

Improvements in the sensibility of MSA-GA tool using COFFEE objective function

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)