Ontology-driven urban issues identification from social media.

As cidades em todo o mundo enfrentam muitos problemas diretamente relacionados ao espaço urbano, especialmente nos aspectos de infraestrutura. A maioria desses problemas urbanos geralmente afeta a vida de residentes e visitantes. Por exemplo, as pessoas podem relatar um carro estacionado em uma calçada que está forçando os pedestres a andar na via, ou um enorme buraco que está causando congestionamento. Além de estarem relacionados com o espaço urbano, os problemas urbanos geralmente demandam ações das autoridades municipais. Existem diversas Redes Sociais Baseadas em Localização (LBSN, em inglês) no domínio das cidades inteligentes em todo o mundo, onde as pessoas relatam problemas urbanos de forma estruturada e as autoridades locais tomam conhecimento para então solucioná-los. Com o advento das redes sociais como Facebook e Twitter, as pessoas tendem a reclamar de forma não estruturada, esparsa e imprevisível, sendo difícil identificar problemas urbanos eventualmente relatados. Dados de mídia social, especialmente mensagens do Twitter, fotos e check-ins, tem desempenhado um papel importante nas cidades inteligentes. Um problema chave é o desafio de identificar conversas específicas e relevantes ao processar dados crowdsourcing ruidosos. Neste contexto, esta pesquisa investiga métodos computacionais a fim de fornecer uma identificação automatizada de problemas urbanos compartilhados em mídias sociais. A maioria dos trabalhos relacionados depende de classificadores baseados em técnicas de aprendizado de máquina, como SVM, Naïve Bayes e Árvores de Decisão; e enfrentam problemas relacionados à representação do conhecimento semântico, legibilidade humana e capacidade de inferência. Com o objetivo de superar essa lacuna semântica, esta pesquisa investiga a Extração de Informação baseada em ontologias, a partir da perspectiva de problemas urbanos, uma vez que tais problemas podem ser semanticamente interligados em plataformas LBSN. Dessa forma, este trabalho propõe uma ontologia no domínio de Problemas Urbanos (UIDO) para viabilizar a identificação e classificação dos problemas urbanos em uma abordagem automatizada que foca principalmente nas facetas temática e geográfica. Uma avaliação experimental demonstra que o desempenho da abordagem proposta é competitivo com os algoritmos de aprendizado de máquina mais utilizados, quando aplicados a este domínio em particular.The cities worldwide face with many issues directly related to the urban space, especially in the infrastructure aspects. Most of these urban issues generally affect the life of both resident and visitant people. For example, people can report a car parked on a footpath which is forcing pedestrians to walk on the road or a huge pothole that is causing traffic congestion. Besides being related to the urban space, urban issues generally demand actions from city authorities. There are many Location-Based Social Networks (LBSN) in the smart cities domain worldwide where people complain about urban issues in a structured way and local authorities are aware to fix them. With the advent of social networks such as Facebook and Twitter, people tend to complain in an unstructured, sparse and unpredictable way, being difficult to identify urban issues eventually reported. Social media data, especially Twitter messages, photos, and check-ins, have played an important role in the smart cities. A key problem is the challenge in identifying specific and relevant conversations on processing the noisy crowdsourced data. In this context, this research investigates computational methods in order to provide automated identification of urban issues shared in social media streams. Most related work rely on classifiers based on machine learning techniques such as Support Vector Machines (SVM), Naïve Bayes and Decision Trees; and face problems concerning semantic knowledge representation, human readability and inference capability. Aiming at overcoming this semantic gap, this research investigates the ontology-driven Information Extraction (IE) from the perspective of urban issues; as such issues can be semantically linked in LBSN platforms. Therefore, this work proposes an Urban Issues Domain Ontology (UIDO) to enable the identification and classification of urban issues in an automated approach that focuses mainly on the thematic and geographical facets. Experimental evaluation demonstrates the proposed approach performance is competitive with most commonly used machine learning algorithms applied for that particular domain.CNP

SBE16 Brazil & Portugal - Sustainable Urban Communities towards a Nearly Zero Impact Built Environment

Vol. IThe organizers of SBE 16 Brazil & Portugal were challenged to promote discussions and the development of solutions for an important and, at the same time, very ambitious topic ? Sustainable Urban Communities towards a Nearly Zero Impact Built Environment. This is the main focus of the international conference SBE16 Brazil & Portugal; the only event of the SBE16/17 conference series being held in Latin America, more precisely, in Vitória (Espírito Santo), Brazil, from the 7th until the 9th of September 2016. The conference offered a unique opportunity to bring together researchers from all over the world to share evidence-based knowledge in the field and succeeded to achieve its goals since many contributions from various parts of the planet were received, addressing a tiny part of the problem or trying to perform the difficult task of making the sum of the parts a coherent whole.info:eu-repo/semantics/publishedVersio

SBE16 Brazil & Portugal - Sustainable Urban Communities towards a Nearly Zero Impact Built Environment

Vol. IThe organizers of SBE 16 Brazil & Portugal were challenged to promote discussions and the development of solutions for an important and, at the same time, very ambitious topic ? Sustainable Urban Communities towards a Nearly Zero Impact Built Environment. This is the main focus of the international conference SBE16 Brazil & Portugal; the only event of the SBE16/17 conference series being held in Latin America, more precisely, in Vitória (Espírito Santo), Brazil, from the 7th until the 9th of September 2016. The conference offered a unique opportunity to bring together researchers from all over the world to share evidence-based knowledge in the field and succeeded to achieve its goals since many contributions from various parts of the planet were received, addressing a tiny part of the problem or trying to perform the difficult task of making the sum of the parts a coherent whole.info:eu-repo/semantics/publishedVersio

Weiterentwicklung analytischer Datenbanksysteme

This thesis contributes to the state of the art in analytical database systems. First, we identify and explore extensions to better support analytics on event streams. Second, we propose a novel polygon index to enable efficient geospatial data processing in main memory. Third, we contribute a new deep learning approach to cardinality estimation, which is the core problem in cost-based query optimization.Diese Arbeit trägt zum aktuellen Forschungsstand von analytischen Datenbanksystemen bei. Wir identifizieren und explorieren Erweiterungen um Analysen auf Eventströmen besser zu unterstützen. Wir stellen eine neue Indexstruktur für Polygone vor, die eine effiziente Verarbeitung von Geodaten im Hauptspeicher ermöglicht. Zudem präsentieren wir einen neuen Ansatz für Kardinalitätsschätzungen mittels maschinellen Lernens