Evaluating decision-making performance in a grid-computing environment using DEA

Energy saving involves two direct benefits: sustainability and cost reduction, both of which Information Technologies must be aware. In this context, clusters, grids and data centres represent the hungriest con sumers of energy. Energy-saving policies for these infrastructures must be applied in order to maximize their resources. The aim of this paper is to compare how efficient these policies are in each location of a grid infrastructure. By identifying efficient policies in each location and the slack in inputs and outputs of the inefficient locations, Data Envelopment Analysis presents a very useful technique for comparing and improving efficiency level. This work enables managers to uncover any misuse of resources so that cor rective action can be taken.Ministerio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon

A Model for Qualitative Colour Description and Comparison

A model for Qualitative Colour Description and Comparison (QCDC) is presented in this paper. Using Hue Saturation and Lightness colour space, qualitative colours are defined in general distinguishing rainbow colours, pale, light, dark colours and colours in the grey scale. The relational structure or the conceptual neighbourhood of our qualitative colour model is analysed and used to formu- late a measure of similarity between colour names. This measure of similarity is proved to solve abso- lute and relative comparison of qualitative colours. Finally the cognitive adequacy of the QCDC model is analysed.Ministerio de Ciencia e Innovación TIN2009-14378- C02-0

Defining Adaptive Learning Paths For Competence-Oriented Learning

This paper presents a way to describe educational itineraries in a competence-oriented learning system in order to solve the problem of sequencing several independent courses. The main objective is to extract adaptive learning paths composed by the subset of needed courses passed in the right order. This approach improves the courses’ re-usability allowing courses to be included in different itineraries, improving the re-usability of the courses, and making possible the definition of mechanisms to adapt the learning path to the learner’s needs in execution tim

Design of a Functional Architecture for the Management of Cluster Resources and Services through the Web

In 2006 Junta de Andalucía created the Andalusian Supercomputing Network (RASCI). RASCI consists of supercomputing nodes distributed geographically throughout Andalusia that provide the region with a large number of computing resources. Increased network bandwidth, more powerful computers and acceptance of the Internet have driven a growth in demand for new and better ways to utilize high-performance technical computing (HPTC) resources

Ameva: An autonomous discretization algorithm

This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a potentially minimal number of discrete intervals. Its most important advantage, in contrast with several existing discretization algorithms, is that it does not need the user to indicate the number of intervals. We have compared Ameva with one of the most relevant discretization algorithms, CAIM. Tests performed comparing these two algorithms show that discrete attributes generated by the Ameva algorithm always have the lowest number of intervals, and even if the number of classes is high, the same computational complexity is maintained. A comparison between the Ameva and the genetic algorithm approaches has been also realized and there are very small differences between these iterative and combinatorial approaches, except when considering the execution time.Ministerio de Educación y Ciencia TSI2006-13390-C02-02Junta de Andalucía P06-TIC-0214

Support vector machines for classification of input vectors with different metrics

In this paper, a generalization of support vector machines is explored where it is considered that input vectors have different ℓp norms for each class. It is proved that the optimization problem for binary classification by using the maximal margin principle with ℓp and ℓq norms only depends on the ℓp norm if 1 ≤ p ≤ q. Furthermore, the selection of a different bias in the classifier function is a consequence of the ℓq norm in this approach. Some commentaries on the most commonly used approaches of SVM are also given as particular cases

Trip destination prediction based on past GPS log using a Hidden Markov Model

In this paper, a system based on the generation of a Hidden Markov Model from the past GPS log and cur- rent location is presented to predict a user’s destination when beginning a new trip. This approach dras- tically reduces the number of points supplied by the GPS device and it permits a ‘‘support-map” to be generated in which the main characteristics of the trips for each user are taken into account. Hence, in contrast with other similar approaches, total independence from a street-map database is achievedMinisterio de Educación y Ciencia TSI2006–13390-C02–02Junta de Andalucia TIC214

A probabilistic tri-class Support Vector Machine

c 2010 JPRR. All rights reserved. Permissions to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or to republish, requires a fee and/or special permission from JPRR. La publicació original està disponible en www.jprr.orgA probabilistic interpretation for the output obtained from a tri-class Support Vector Machine into a multi-classification problem is presented in this paper. Probabilistic outputs are defined when solving a multi-class problem by using an ensemble architecture with tri-class learning machines working in parallel. This architecture enables the definition of an ‘interpretation’ mapping which works on signed and probabilistic outputs providing more control to the user on the classification problem.Peer ReviewedPostprint (published version