An Information Theoretic Approach to Automatic Query Expansion

International audience

Acesso à alimentação como direito social no Brasil : implicações para a sociedade e para o sistema nacional de informações

A Emenda Constitucional 64/2010 garantiu Direito Humano à Alimentação como direito básico e social, alterando o Artigo 6º da Constituição Federal. O artigo analisa as significativas implicações desta alteração na gestão das políticas públicas brasileiras Ao assegurar o Direito à Alimentação como direito básico e social, a Carta constituiu um dever, ou uma obrigação positiva do Estado brasileiro. O artigo discute também o significado desta mudança para o sistema brasileiro de informações, argumentando que já existem fontes de dados e sistema de indicadores construídos para o monitoramento consistente das situações de (in)segurança alimentar e nutricional no país, restando agora ao governo federal e aos gestores do Sistema Brasileiro de Informações Estatísticas e Geográficas definir a regularidade e a frequência da aplicação e divulgação destes instrumentos. Nossa atenção se concentrará basicamente nas possibilidades de uso da Pesquisa de Orçamentos Familiares e da Pesquisa Nacional por Amostra Domiciliar como fontes de dados. _______________________________________________________________________________ ABSTRACT

Potencialidades de cultivo de macroalgas no litoral do Rio Grande do Norte (NE, Brasil): uma perspectiva de integração de dados ambientais em sistemas de informação geográfica (SIG)

Since centuries ago, the Asians use seaweed as an important source of feeding and are their greatest world-wide consumers. The migration of these peoples for other countries, made the demand for seaweed to increase. This increasing demand prompted an industry with annual values of around US$ 6 billion. The algal biomass used for the industry is collected in natural reservoirs or cultivated. The market necessity for products of the seaweed base promotes an unsustainable exploration of the natural banks, compromising its associated biological balance. In this context, seaweed culture appears as a viable alternative to prevent the depletion of these natural supplies. Geographic Information Systems (GIS) provide space and produce information that can facilitate the evaluation of important physical and socio-economic characteristics for the planning of seaweed culture. This objective of this study is to identify potential coastal areas for seaweed culture in the state of Rio Grande do Norte, from the integration of social-environmental data in the SIG. In order to achieve this objective, a geo-referred database composed of geographical maps, nautical maps and orbital digital images was assembled; and a bank of attributes including physical and oceanographical variables (winds, chains, bathymetry, operational distance from the culture) and social and environmental factors (main income, experience with seaweed harvesting, demographic density, proximity of the sheltered coast and distance of the banks) was produced. In the modeling of the data, the integration of the space database with the bank of attributes for the attainment of the map of potentiality of seaweed culture was carried out. Of a total of 2,011 ha analyzed by the GIS for the culture of seaweed, around 34% or 682 ha were indicated as high potential, 55% or 1,101 ha as medium potential, and 11% or 228 ha as low potential. The good indices of potentiality obtained in the localities studied demonstrate that there are adequate conditions for the installation of seaweed culture in the state of Rio Grande do Norte

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.