David Sánchez

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In this paper we present a methodology to build automatically an ontology, extracting information from the World Wide Web from an initial keyword. This ontology represents a taxonomy of classes and gives to the user a general view of the kind of concepts and the most significant sites that he can find on the Web for the specified keyword's domain. The(More)
The European Union FP7 CHRON project addresses the challenge of controlling and managing the next generation of heterogeneous optical networks supporting the Future Internet. For that aim, the CHRON project proposes a Cognitive Heterogeneous Reconfigurable Optical Network, which observes, acts, learns and optimizes its performance. The core element of such(More)
This paper reports on the first user/application-driven multi-technology optical sub-wavelength network for intra/inter Data-Centre (DC) communications. Two DCs each with distinct sub-wavelength switching technologies, frame based synchronous TSON and packet based asynchronous OPST are interconnected by a WSON inter-DC communication. The intra/inter DC(More)
“Greening the Internet” is an important research topic in the last years. The Internet capacity and energy consumption have increased, and the utilization of design and operation techniques to reduce this consumption are a must. In this paper, we present a multiobjective genetic algorithm to design virtual topologies for reconfigurable(More)
This paper studies the Non-Line-Of-Sight condition mitigation issue in mobile subscriber positioning systems by weighting Time-Of-Arrival measures and applying geometrical restrictions. Particularly, this work departs from a more exact characterization of the signal statistics to achieve weighting factors able to reach a more effective mitigation, and(More)
We have recently proposed a cognitive Quality of Transmission (QoT) Estimator for classifying lightpaths into high or low quality categories. In this paper, we enhance that work by incorporating learning and forgetting techniques with the aim of optimizing the underlying knowledge base (KB) on which the cognitive estimator relies. We demonstrate that by(More)
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