As the spatial information industry moves from an era of data collection to one of data maintenance, new integration methods to consolidate or to update datasets are required. These must reduce the discrepancies that are becoming increasingly apparent when spatial datasets are overlaid. It is essential that any such methods consider the quality characteristics of, firstly, the data being integrated and, secondly, the resultant data. This thesis develops techniques that give due consideration to data quality during the integration process.

Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-05-21T13:33:00Z No. of bitstreams: 1 JOSÉ AMILTON MOURA ACIOLI FILHO - DISSERTAÇÃO (PPGCC) 2016.pdf: 4531903 bytes, checksum: 0544920547c2d257f657b480a1c5f45f (MD5) / Made available in DSpace on 2018-05-21T13:33:00Z (GMT). No. of bitstreams: 1 JOSÉ AMILTON MOURA ACIOLI FILHO - DISSERTAÇÃO (PPGCC) 2016.pdf: 4531903 bytes, checksum: 0544920547c2d257f657b480a1c5f45f (MD5) Previous issue date: 2016-09-02 / Com a evolução da web e dos sistemas de informação, as organizações têm obtido dados dos mais diversos formatos, estruturas e tipos, podendo-se destacar os espaciais. Devido aos dados apresentarem características distintas, estes acabam sendo mantidos em fontes de dados heterogêneas, sendo assim necessário investir cada vez mais em soluções que possam integrar e analisar estes dados de diferentes fontes. Algumas destas soluções conseguem analisar o componente espacial dos dados, no entanto, essa análise dos dados espaciais é limitada pelo tipo de dados ou funções espaciais suportadas. Neste trabalho, é abordado o problema da integração de dados espaciais de fontes de dados heterogêneas, com ou sem esquema, utilizando linguagem SQL. Este é um problema em aberto na área de integração de dados espaciais, pois as soluções existentes apresentam inúmeras limitações, a exemplo da linguagem de consulta utilizada, os meios para acesso a dados, as tecnologias que podem ser integradas, as funções disponibilizadas e os tipos de dados espaciais suportados. Visando solucionar esse problema, desenvolveu-se a solução GeoDrill, uma extensão do Apache Drill que dá suporte a todas as funções espaciais padronizadas pela OGC (Open Geospatial Consortium), através da linguagem SQL, podendo realizar consultas em dados com ou sem esquema. Para validar a capacidade de integração dos dados no GeoDrill, foi desenvolvido um experimento para analisar as funcionalidades e o desempenho do mesmo. A solução GeoDrill foi capaz de realizar a integração dos dados espaciais de fontes heterogêneas, apresentando-se como uma alternativa para a resolução de parte das limitações existentes na área. / With the evolution of the web and information systems, organizations have obtained data of various formats, structures and types, specially the spatial one. Due to different characteristics presented in data, such data have been stored in heterogeneous data sources. Therefore, it is needed to increasingly invest in solutions that can integrate and analyze these data from different sources. Some of these solutions can analyze the spatial component of data; however, this analysis of spatial data is limited either by the data type or spatial functions supported. In this work, the problem of spatial data integration from heterogeneous data sources is addressed, either with or without using schemas, using SQL language. This is an open issue in the area of spatial data integration, since existing solutions present many limitations, such as the query language used, the ways to access data, the technologies that can be integrated, the available functions set and the spatial data types supported. Aiming at solving this problem, the GeoDrill solution was developed, which is an extension of the Apache Drill that supports all standard spatial functions provided by the OGC (Open Geospatial Consortium) through the SQL language. The GeoDrill can perform queries on data with or without schema. In order to validate the capacity of GeoDrill to integrate data, an experiment was conducted to analyze its functionalities and performance. The obtained results indicate the GeoDrill solution is able to integrate spatial data from heterogeneous data sources. Hence, it appears to be a suitable alternative for solving part of the existing limitations in this research field.

Les systèmes de transport sont un des leviers puissants du progrès de toute société. Récemment les modes de déplacement ont évolué significativement et se diversifient. Les distances quotidiennement parcourues par les citoyens ne cessent d'augmenter au cours de ces dernières années. Cette évolution impacte l'attractivité et la compétitivité mais aussi la qualité de vie grandement dépendante de l'évolution des mobilités des personnes et des marchandises. Les gouvernements et les collectivités territoriales développent de plus en plus des politiques d'incitation à l'éco-mobilité. Dans cette thèse nous nous concentrons sur les systèmes de transport public. Ces derniers évoluent continuellement et offrent de nouveaux services couvrant différents modes de transport pour répondre à tous les besoins des usagers. Outre les systèmes de transports en commun, prévus pour le transport de masse, de nouveaux services de mobilité ont vu le jour, tels que le transport à la demande, le covoiturage planifié ou dynamique et l'autopartage ou les vélos en libre-service. Ils offrent des solutions alternatives de mobilité et pourraient être complémentaires aux services traditionnels. Cepandant, ces services sont à l'heure actuelle isolés du reste des modes de transport et des solutions multimodales. Ils sont proposés comme une alternative mais sans intégration réelle aux plans proposés par les outils existants. Pour permettre la multimodalité, le principal challenge de cette thèse est l'intégration de données et/ou de services provenant de systèmes de transports hétérogènes. Par ailleurs, le concept de données ouvertes est aujourd'hui adopté par de nombreuses organisations publiques et privées, leur permettant de publier leurs sources de données sur le Web et de gagner ainsi en visibilité. On se place dans le contexte des données ouvertes et des méthodes et outils du web sémantique pour réaliser cette intégration, en offrant une vue unifiée des réseaux et des services de transport. Les verrous scientifiques auxquels s'intéresse cette thèse sont liés aux problèmes d'intégration à la fois des données et des services informatiques des systèmes de transport sous-jacents. / Multimodality requires the integration of heterogeneous transportation data and services to construct a broad view of the transportation network. Many new transportation services (e.g. ridesharing, car-sharing, bike-sharing) are emerging and gaining a lot of popularity since in some cases they provide better trip solutions.However, these services are still isolated from the existing multimodal solutions and are proposed as alternative plans without being really integrated in the suggested plans. The concept of open data is raising and being adopted by many companies where they publish their data sources to the web in order to gain visibility. The goal of this thesis is to use these data to enable multimodality by constructing an extended transportation network that links these new services to existing ones.The challenges we face mainly arise from the integration problem in both transportation services and transportation data