La Web Semántica y los archivos de televisión: estado de la cuestión

Se analizan y describen las principales líneas de trabajo de la Web Semántica en el ámbito de los archivos de televisión. Para ello, se analiza y contextualiza la web semántica desde una perspectiva general para posteriormente analizar las principales iniciativas que trabajan con lo audiovisual: Proyecto MuNCH, Proyecto S5T, Semantic Television y VideoActive.

Targeting heterogeneous architectures via macro data flow

We propose a data flow based run time system as an efficient tool for supporting execution of parallel code on heterogeneous architectures hosting both multicore CPUs and GPUs. We discuss how the proposed run time system may be the target of both structured parallel applications developed using algorithmic skeletons/parallel design patterns and also more "domain specific" programming models. Experimental results demonstrating the feasibility of the approach are presented. © 2012 World Scientific Publishing Company.

Two non-synonymous markers in PTPN21, identified by genome-wide association study data-mining and replication, are associated with schizophrenia

We conducted data-mining analyses of genome wide association (GWA) studies of the CATIE and MGS-GAIN datasets, and found 13 markers in the two physically linked genes, PTPN21 and EML5, showing nominally significant association with schizophrenia. Linkage disequilibrium (LD) analysis indicated that all 7 markers from PTPN21 shared high LD (r(2)>0.8), including rs2274736 and rs2401751, the two non-synonymous markers with the most significant association signals (rs2401751, P=1.10 × 10(-3) and rs2274736, P=1.21 × 10(-3)). In a meta-analysis of all 13 replication datasets with a total of 13,940 subjects, we found that the two non-synonymous markers are significantly associated with schizophrenia (rs2274736, OR=0.92, 95% CI: 0.86-0.97, P=5.45 × 10(-3) and rs2401751, OR=0.92, 95% CI: 0.86-0.97, P=5.29 × 10(-3)). One SNP (rs7147796) in EML5 is also significantly associated with the disease (OR=1.08, 95% CI: 1.02-1.14, P=6.43 × 10(-3)). These 3 markers remain significant after Bonferroni correction. Furthermore, haplotype conditioned analyses indicated that the association signals observed between rs2274736/rs2401751 and rs7147796 are statistically independent. Given the results that 2 non-synonymous markers in PTPN21 are associated with schizophrenia, further investigation of this locus is warranted.

A Data Model for Content Modelling of Temporal Media

This paper describes a data model for content representation of temporal media in an IP based sensor network. The model is formed by introducing the idea of semantic-role from linguistics into the underlying concepts of formal event representation with the aim of developing a common event model. The architecture of a prototype system for a multi camera surveillance system, based on the proposed model is described. The important aspects of the proposed model are its expressiveness, its ability to model content of temporal media, and its suitability for use with a natural language interface. It also provides a platform for temporal information fusion, as well as organizing sensor annotations by help of ontologies.

An exploration of integrated data on the social dynamics of suicide among women

The gender based nature of suicide related behaviour is largely accepted.However, studies which report exclusively on female fatal suicides are rare.Here we demonstrate how female fatal suicide has effectively been ‘othered’ and appears ‘incidental’ in studies which compare female behaviour with that of their male counterparts. We highlight how recent studies of suicide have tended to be dominated by male only approaches,which increasingly link issues of masculinity with male death by suicide.Drawing on data collected from the GP and Coroner’s office, we then apply the Sociological Autopsy approach to a cohort of 78 deaths recorded as suicides in the UK between 2007 and 2009. By focusing on females in isolation from males, we demonstrate that as in male suicide only studies,it is similarly possible to draw out issues associated with the feminine identity which can be linked to death by suicide. We identify that bereavement, sexual violence and motherhood could all be linked to the lives and help-seeking of the females who died. In closing, we suggest are orientation towards sociological analytic approaches of female suicide may help to produce further reductions in the rate of female death by suicide.

Anomaly Detection for Data with Spatial Attributes

The problem of detecting spatially-coherent groups of data that exhibit anomalous behavior has started to attract attention due to applications across areas such as epidemic analysis and weather forecasting. Earlier efforts from the data mining community have largely focused on finding outliers, individual data objects that display deviant behavior. Such point-based methods are not easy to extend to find groups of data that exhibit anomalous behavior. Scan Statistics are methods from the statistics community that have considered the problem of identifying regions where data objects exhibit a behavior that is atypical of the general dataset. The spatial scan statistic and methods that build upon it mostly adopt the framework of defining a character for regions (e.g., circular or elliptical) of objects and repeatedly sampling regions of such character followed by applying a statistical test for anomaly detection. In the past decade, there have been efforts from the statistics community to enhance efficiency of scan statstics as well as to enable discovery of arbitrarily shaped anomalous regions. On the other hand, the data mining community has started to look at determining anomalous regions that have behavior divergent from their neighborhood.In this chapter,we survey the space of techniques for detecting anomalous regions on spatial data from across the data mining and statistics communities while outlining connections to well-studied problems in clustering and image segmentation. We analyze the techniques systematically by categorizing them appropriately to provide a structured birds eye view of the work on anomalous region detection;we hope that this would encourage better cross-pollination of ideas across communities to help advance the frontier in anomaly detection.

Comorbidity in severe asthma requiring systemic corticosteroid therapy: cross-sectional data from the Optimum Patient Care Research Database and the British Thoracic Difficult Asthma Registry

Objective: To determine the prevalence of systemic corticosteroid-induced morbidity in severe asthma.
Design: Cross-sectional observational study.Setting The primary care Optimum Patient Care Research Database and the British Thoracic Society Difficult Asthma Registry.
Participants: Optimum Patient Care Research Database (7195 subjects in three age- and gender-matched groups)—severe asthma (Global Initiative for Asthma (GINA) treatment step 5 with four or more prescriptions/year of oral corticosteroids, n=808), mild/moderate asthma (GINA treatment step 2/3, n=3975) and non-asthma controls (n=2412). 770 subjects with severe asthma from the British Thoracic Society Difficult Asthma Registry (442 receiving daily oral corticosteroids to maintain disease control).
Main outcome measures: Prevalence rates of morbidities associated with systemic steroid exposure were evaluated and reported separately for each group.
Results: 748/808 (93%) subjects with severe asthma had one or more condition linked to systemic corticosteroid exposure (mild/moderate asthma 3109/3975 (78%), non-asthma controls 1548/2412 (64%); p<0.001 for severe asthma versus non-asthma controls). Compared with mild/moderate asthma, morbidity rates for severe asthma were significantly higher for conditions associated with systemic steroid exposure (type II diabetes 10% vs 7%, OR=1.46 (95% CI 1.11 to 1.91), p<0.01; osteoporosis 16% vs 4%, OR=5.23, (95% CI 3.97 to 6.89), p<0.001; dyspeptic disorders (including gastric/duodenal ulceration) 65% vs 34%, OR=3.99, (95% CI 3.37 to 4.72), p<0.001; cataracts 9% vs 5%, OR=1.89, (95% CI 1.39 to 2.56), p<0.001). In the British Thoracic Society Difficult Asthma Registry similar prevalence rates were found, although, additionally, high rates of osteopenia (35%) and obstructive sleep apnoea (11%) were identified.

Conclusions: Oral corticosteroid-related adverse events are common in severe asthma. New treatments which reduce exposure to oral corticosteroids may reduce the prevalence of these conditions and this should be considered in cost-effectiveness analyses of these new treatments.

Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Management

Guest reputation indexes to analyze hotel’s online reputation using data extracted from OTAs

Nowadays many travelers use online travel agency (OTAs) to book flights, hotel rooms, rent-a-cars, cruises or entire vacation packages. Usually OTAs allow their users to give scores and to write reviews about what was used. Each OTA defines the terms and conditions for guest rating or review score and hoteliers are giving increasing importance to the scores and reviews their guests do in OTAs. This paper proposes two guest reputation index to help hoteliers to monitorize their presence in OTAs. The Aggregated Guest Reputation Index (AGRI), which shows the positioning of a hotel in different OTAs and it is calculated from the scores obtained by the hotels in those OTAs. Another one, the Semantic Guest Reputation Index (SGRI), which incorporates the social reputation of a hotel and that can be visualized through the development of word clouds or tag clouds. Examples of usage of these indexes are given with data extracted from 5-stars hotels in the Algarve, south region of Portugal, that are available on Booking and Expedia.

Knowledge representation for data integration and exploration in translational medicine

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014

Controlo cognitivo na memória episódica : dados de adolescentes e jovens adultos

Dissertação de mestrado, Ciência Cognitiva, Universidade de Lisboa, Faculdade de Psicologia, Faculdade de Medicina, Faculdade de Ciências, Faculdade de Letras, 2014

Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

Data analysis applied to the evaluation of a technological nature teaching model as a regional development tool

This paper presents the creation and development of technological schools directly linked to the business community and to higher public education. Establishing themselves as the key interface between the two sectors they make a signigicant contribution by having a greater competitive edge when faced with increasing competition in the tradional markets. The development of new business strategies supported by references of excellence, quality and competitiveness also provides a good link between the estalishment of partnerships aiming at the qualification of education boards at a medium level between the technological school and higher education with a technological foundation. We present a case study as an example depicting the success of Escola Tecnológica de Vale de Cambra.