Web service orchestration is widely spread for the creation of composite web services using standard specifications such as BPEL4WS. The myriad of specifications and aspects that should be considered in orchestrated web services are resulting in increasing complexity. This complexity leads to software infrastructures difficult to maintain with interwoven code involving different aspects such as security, fault tolerance, distribution, etc. In this paper, we present ZenFlow a reflective BPEL engine that enables to separate the implementation of different aspects among them and from the implementation of the regular orchestration functionality of the BPEL engine. We illustrate its capabilities and performance exercising the reflective interface through a decentralized orchestration use case.
Research based on indoor location systems has recently been developed due to growing interest in locationaware services to be implemented in light mobile devices. Most of this work is based on received signal strength (RSS) from access points. However, a major drawback from using RSS is its variability due to indoor multipath effect caused by reflection, diffraction and scattering of signal propagation. Therefore, different device orientations in a fixed location provide significant and different RSS values. In this paper, we propose to extend fingerprinting with device orientation information. Implementation of our location system is based on data mining techniques employing decision tree algorithms. Experimental results demonstrate that using RSS samples with the device orientation information improves the location estimation with high accuracy.
Resumen: La clasificación de textos, en entornos en los que el volumen de datos a clasificar es tan elevado que resulta muy costosa la realización de esta tarea por parte de humanos, requiere la utilización de clasificadores de textos en lenguaje natural automáticos. El clasificador propuesto en el presente estudio toma como base la Wikipedia para la creación del corpus que define una categoría mediante técnicas de Procesado de Lenguaje Natural (PLN) que analizan sintácticamente los textos a clasificar. El resultado final del sistema propuesto presenta un alto porcentaje de acierto, incluso cuando se compara con los resultados obtenidos con técnicas alternativas de Aprendizaje Automático.Palabras clave: Categorización de textos; Wikipedia; tf-idf; Aprendizaje Automático; Procesado de Lenguaje Natural.Abstract: Automatic Text Classifiers are needed in environments where the amount of data to handle is so high that human classification would be ineffective. In our study, the proposed classifier takes advantage of the Wikipedia to generate the corpus defining each category. The text is then analyzed syntactically using Natural Language Processing software. The proposed classifier is highly accurate and outperforms Machine Learning trained classifiers.
One of the main tasks of the information services is to help users to find information that satisfies their preferences reducing their search effort. Recommendation systems filter information and only show the most preferred items. Ontologies are fundamental elements of the Semantic Web and have been exploited to build more accurate and personalized recommendations by inferencing missing user preferences. With catalogs changing continuously ontologies must be built autonomously without expert intervention. In this paper we present an audiovisual recommendation engine which uses an enhanced ontology filtering technique to recommend audiovisual content. Experimental results show that the improvements of the ontology filtering technique generate accurate recommendations. In this section we present the scenario where the recommendation engine has been developed, and the ontology filtering technique.
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