A model driven framework for geographic knowledge discovery

Geographic knowledge discovery (GKD) is the process of extracting information and knowledge from massive georeferenced databases. Usually the process is accomplished by two different systems, the Geographic Information Systems (GIS) and the data mining engines. However, the development of those systems is a complex task due to it does not follow a systematic, integrated and standard methodology. To overcome these pitfalls, in this paper, we propose a modeling framework that addresses the development of the different parts of a multilayer GKD process. The main advantages of our framework are that: (i) it reduces the design effort, (ii) it improves quality systems obtained, (iii) it is independent of platforms, (iv) it facilitates the use of data mining techniques on geo-referenced data, and finally, (v) it ameliorates the communication between different users.

Concepciones sobre las tecnologías de la información y la comunicación (TIC) y sus implicaciones educativas: un estudio exploratorio con profesorado de la provincia de Ñuble, Chile

El propósito de este estudio fue conocer las concepciones de profesores de educación primaria sobre las tecnologías educativas en dos dimensiones: a) conocimiento de las tecnologías de la información y comunicación; y b) creencias sobre el uso educativo que el profesorado da a estas herramientas. Se opta por un enfoque metodológico cuantitativo y un diseño no-experimental descriptivo del tipo encuesta. El análisis de los datos se realizó mediante paquete estadístico SPSS 14.0 y las técnicas utilizadas fueron descriptivos, frecuencias y porcentajes, técnicas de reducción de datos (análisis factorial) e inferencia estadística (comparación de medias y porcentajes). Los resultados demuestran que la mayoría de los profesores reconocen el interés que las tecnologías despiertan en el alumnado y las oportunidades de aprendizaje que ofrecen principalmente en relación con los diferentes ritmos de aprendizaje y las necesidades educativas especiales. Se identifica la búsqueda de información como una competencia fundamental, así como se evidencia que existe una relación causal entre el nivel de formación, la importancia que el profesor otorga al recurso y el uso educativo. Los resultados hacen aconsejable promover programas de formación continua en esta área y fortalecer la formación inicial docente.

The Gaia-ESO Survey: processing FLAMES-UVES spectra

The Gaia-ESO Survey is a large public spectroscopic survey that aims to derive radial velocities and fundamental parameters of about 105 Milky Way stars in the field and in clusters. Observations are carried out with the multi-object optical spectrograph FLAMES, using simultaneously the medium-resolution (R ~ 20 000) GIRAFFE spectrograph and the high-resolution (R ~ 47 000) UVES spectrograph. In this paper we describe the methods and the software used for the data reduction, the derivation of the radial velocities, and the quality control of the FLAMES-UVES spectra. Data reduction has been performed using a workflow specifically developed for this project. This workflow runs the ESO public pipeline optimizing the data reduction for the Gaia-ESO Survey, automatically performs sky subtraction, barycentric correction and normalisation, and calculates radial velocities and a first guess of the rotational velocities. The quality control is performed using the output parameters from the ESO pipeline, by a visual inspection of the spectra and by the analysis of the signal-to-noise ratio of the spectra. Using the observations of the first 18 months, specifically targets observed multiple times at different epochs, stars observed with both GIRAFFE and UVES, and observations of radial velocity standards, we estimated the precision and the accuracy of the radial velocities. The statistical error on the radial velocities is σ ~ 0.4 km s-1 and is mainly due to uncertainties in the zero point of the wavelength calibration. However, we found a systematic bias with respect to the GIRAFFE spectra (~0.9 km s-1) and to the radial velocities of the standard stars (~0.5 km s-1) retrieved from the literature. This bias will be corrected in the future data releases, when a common zero point for all the set-ups and instruments used for the survey is be established.

S-SETA: Selective Software-Only Error-Detection Technique Using Assertions

Software-based techniques offer several advantages to increase the reliability of processor-based systems at very low cost, but they cause performance degradation and an increase of the code size. To meet constraints in performance and memory, we propose SETA, a new control-flow software-only technique that uses assertions to detect errors affecting the program flow. SETA is an independent technique, but it was conceived to work together with previously proposed data-flow techniques that aim at reducing performance and memory overheads. Thus, SETA is combined with such data-flow techniques and submitted to a fault injection campaign. Simulation and neutron induced SEE tests show high fault coverage at performance and memory overheads inferior to the state-of-the-art.

Empowering global software development with business intelligence

Context: Global Software Development (GSD) allows companies to take advantage of talent spread across the world. Most research has been focused on the development aspect. However, little if any attention has been paid to the management of GSD projects. Studies report a lack of adequate support for management’s decisions made during software development, further accentuated in GSD since information is scattered throughout multiple factories, stored in different formats and standards. Objective: This paper aims to improve GSD management by proposing a systematic method for adapting Business Intelligence techniques to software development environments. This would enhance the visibility of the development process and enable software managers to make informed decisions regarding how to proceed with GSD projects. Method: A combination of formal goal-modeling frameworks and data modeling techniques is used to elicitate the most relevant aspects to be measured by managers in GSD. The process is described in detail and applied to a real case study throughout the paper. A discussion regarding the generalisability of the method is presented afterwards. Results: The application of the approach generates an adapted BI framework tailored to software development according to the requirements posed by GSD managers. The resulting framework is capable of presenting previously inaccessible data through common and specific views and enabling data navigation according to the organization of software factories and projects in GSD. Conclusions: We can conclude that the proposed systematic approach allows us to successfully adapt Business Intelligence techniques to enhance GSD management beyond the information provided by traditional tools. The resulting framework is able to integrate and present the information in a single place, thereby enabling easy comparisons across multiple projects and factories and providing support for informed decisions in GSD management.