Ontology-Based Approach for Application Integration

http://interop-esa05.unige.ch/INTEROP/Proceedings/Doctoral/PerPaper/II-1-Izza.pdfInternational audienceEnterprise Application Integration (EAI) is still facing the crucial semantic integration problem. This latter is not correctly addressed by today's EAI solutions that focus mainly on the technical and syntactical integration. Addressing the semantic integration level will promote EAI by providing it more consistency and robustness. Some efforts are suggested to solve the semantic integration problem, but they are still not mature. This paper deals with semantic problem in EAI and will present an ontology-based approach in order to overcome some issues of the integration problem

Model based semantic enterprise application integration

U sklopu doktorske disertacije izvršeno je istraživanje vezano za automatizaciju integracije poslovnih aplikacija. Prikazani pristup kombinuje model strukture interfejsa aplikacija koje se integrišu sa formalnim opisom njihove semantike, datim u vidu ontologije. Na osnovu ovih izvora, obavlja se automatsko mapiranje među elementima interfejsa koji se integrišu, kao i detekcija i razrešavanje semantičkih konflikata. Razvijena je praktična implementacija predloženog pristupa, koja je korišćena za verifikaciju teorijskih razmatranja, a uključuje adaptivan radni okvir i jezik specifičan za domen. Predloženi pristup je verifikovan na dva realna integraciona scenarija i jednim eksperimentom.This thesis presents a research in the field of automation of enterprise application integration. The approach combines structural models of interfaces of the applications being integrated with a formal specification of their semantics, given in form of an ontology. Using information from these sources, automated interface mapping is performed, along with detection and resolution of semantic conflicts. A practical implementation of the presented approach was developed and used to verify theoretical considerations. The implementation includes an adaptive framework and a domain specific language. The proposed approach has been verified on two real-world integration scenarios and one experiment

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Semantic Service Matching in the Context of ODSOI Project

ISBN 978-1-84800-220-3International audienceMatching services still constitutes a big challenge for most of enterprises in general and notably for large and dynamic ones. This paper delineates a service similarity approach in the context of ODSOI (Ontology-Driven Service-Oriented Integration) project that concern the intra-enterprise integration issues in the field of manufacturing industry. Our approach is based on an extension of OWL-S service similarity. It proposes a rigorous quantitative ranking method based on some novel semantic similarity degrees. An implementation of this ranking method is provided in the form of a prototype coded on Java platform exploiting some existing APIs mainly Racer OWL API, and OWL-S-API