Vicente Botón-Fernández

Linked Data methodologies for managing information about television content: Applying Linked Data principles in the OntoTV system, in order to improve the collection processes and the way television information is accessed

OntoTV is a television information management system designed for improving the quality and quantity of the formation available in the current television platforms. In order to achieve this objective, OntoTV (1) collects the information offered by the broadcasters, (2) integrates it into a ontology-based data structure, (3) extracts extra data from alternative television sources, and (4) makes possible for the user to perform queries over the stored information. This document shows the way Linked Data methodologies have been applied in OntoTV system, and the improvements in the data consumption and publication processes that have been obtained as result. By the one hand, the possibility of accessing to information available in the Web of Data has made possible to offer more complete descriptions about the programs, as well as more detailed guides than those obtained by using classic collection methods. On the other hand, as the information of the television programs and channels are published according to the Linked Data philosophy, it becomes available not only for OntoTV clients, but also for other agents able to access Linked Data resources, who could offer the viewer more fresh and innovative features.

Analysis of Sequential Events for the Recognition of Human Behavior Patterns in Home Automation Systems

Learning users’ frequent patterns is very useful to develop real human-centered environments. By analyzing the occurrences of events over time in a home automation system, it’s possible to find periodic patterns based on action-time relationships. However, the human behavior could be better defined if it’s related to chained actions, creating action-action relationships. This work presents IntelliDomo’s learning layer, a data mining approach based on ontologies and production rules that aims to achieve those objectives. This module is able to acquire users’ habits and automatically generate production rules for behavior patterns to anticipate the user’s periodic actions. The learning layer includes new features looking for the adaptability and personification of the environment.

Mining sequential patterns to efficiently manage Energy Storage Systems within smart home buildings

This paper introduces a Smart Energy Management System capable of controlling energy and smart home devices to optimize the power consumption of several buildings individually and smooth consumption peaks by means of a central system. The proposed model consists of two main systems; the Energy Storage Units and the Central Energy Management System. The first ones are able to store energy, communicate with X10 devices and transmit information regarding household consumption. Finally, the Central System is in charge of controlling the Energy Storage Units, analyzing the information provided through data mining techniques and deciding the best operation strategy for each unit, so that they can smooth their load curves. The strategy consists of a set of semantic rules, based on sequential patterns, which aim to avoid peaks by shutting down low-priority X10 devices and using properly the energy stored.

Word Clouds as a Learning Analytic Tool for the Cooperative e-Learning Platform NeuroK.

Word cloud or tag cloud is very popular these days. It is a tool used to display text data summarization in a visual way very easy to understand. However, it has not been extensively used in teaching, especially in elearning, where it would make a differential advantage. This research presents the definition and implementation of a word cloud tool in a social network-based e-learning platform (NeuroK), which is based on the principles of neurodidactics. The different features developed and the results are shown. Several options to compare word clouds from students and teachers allow the teacher to follow the development of the course, and they provide him more information to facilitate the evaluation process.

Graphs and Key Players in an Educational Social Network.

A new social learning graph tool has been proposed and implemented in NeuroK. This is an interactive visualization of the relationships among students, based on their comments, favorites, mentions and rates in a course or a learning unit. Some illustrative examples are provided showing its possibilities, challenges and future potential of using the social graph tool. Social graph can help teachers and students to have an easy visual image of their relationships, discovering the key players in the network and also the isolated students in risk of dropping out. The teacher can use this information to reach the students “in risk” and try to retain them with some motivating and engaging actions. The social graph and the indices that can be obtained from it show up to be also a very good tool to analyse the development of the course and to help the teacher to evaluate the students.

Intelligent Energy Management System for the Optimization of Power Consumption

This paper describes a Smart Storage System capable of managing energy and smart home devices to optimize the local power consumption of a house. The proposed model consists of two main systems; the Local Energy Management Unit (LEMU) and the Central Energy Management and Intelligent System (CEMIS). On the one hand, the LEMU is able to maintain the power consumption under a maximum reference value and to switch on/off the devices by using domotic protocols. On the other hand, the CEMIS receives operation data remotely from several devices, analyzes them using intelligent techniques and determines the best operation strategy for each LEMU, communicating the operation references back to them.

Intelligent Energy Management System for Residential and Community Applications

This paper presents a Smart Storage System able to manage the energy and the smart home devices of a house for optimizing the local consumption of energy, even if there is not renewable generation. The proposed system is composed by two main systems. On the one hand, the Local Energy Management units that will be located in the houses, which are able to maintain the power consumption under a maximum reference value and to switch on/off the devices in the house by using domotic protocols. On the other hand, the Central Energy Management and Intelligent System, that receives operation data from each local unit, analyzes them using behavioral and optimization algorithms and determines the best way in which each local unit has to operate, communicating the operation references back to them.