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Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.

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Nowadays, Spatial Data Infrastructures (SDIs) play an important role in government agencies, at different levels: global, national, and local. They aim to improve the management and sharing of geospatial data. Nonetheless, these SDIs have been developed as information islands, in which a user's query is compared to metadata described only in their own catalog services. The lack of interaction among SDIs limits the potential of these infrastructures in providing geospatial data to a larger audience. This article presents a distributed architecture, based on a federation of SDIs which interact among themselves, using query propagation. This propagation facilitates data discovery and sharing. We also describe a distributed query processing service used to enable the resource discovery in distributed infrastructures.

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<p>Os índices elevados de evasão escolar representam um problema que tem atraído a atenção de muitos gestores e educadores ao redor do mundo. Esse problema acontece quando, por algum motivo, o aluno abandona o curso no qual está matriculado antes de sua conclusão. A evasão escolar gera uma série de prejuízos aos diversos atores envolvidos no processo educacional e pode ocorrer em diversos níveis e modalidades de ensino e por diversas causas. Uma forma de se combater a evasão escolar e, consequentemente, os problemas causados por ela consiste em identificar de forma antecipada, alunos ou perfis de alunos com alto risco de evasão. A identificação prévia destes perfis pode auxiliar os gestores de políticas educacionais na tomada de decisão, como a elaboração de planos e a execução de ações que tentem evitar que os alunos abandonem os seus cursos. Neste trabalho propõem a confecção de um banco de dados dimensional utilizado em um <em>Data Warehouse</em>, para análise das características dos alunos, relacionadas à evasão escolar. Os dados são oriundos da Plataforma Nilo Peçanha, adquiridos através de processos de extração, transformação e carga dos dados. Processo de  Descoberta de Conhecimento em Banco Dados foi utilizado para analisar quais características estão relacionadas à evasão escolar de alunos de cursos de nível técnico e de graduação da rede federal de ensino de todo o Brasil, comparando os resultados com os dados do Instituto Federal de Educação, Ciência e Tecnologia da Paraíba (IFPB). As características relacionadas à faixa etária, renda per capita familiar e etnia apresentaram diferenças significativas quando comparados entre os alunos que abandonaram o curso daqueles que concluíram.</p>

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Currently, spatial data infrastructures (SDIs) are becoming the solution adopted by many organizations to facilitate discovery, access and integration of geographic information produced and provided by different agencies. However, the catalog services currently offered by these infrastructures provide keyword-based queries only. This may result on low recall and precision. Furthermore, these catalogs retrieve information based on the metadata records that describe either a service or a dataset. This feature brings limitations to more specific information discovery, such as those based on feature types and instances. This chapter proposes a solution that aims to overcome these limitations by using multiple ontologies to enhance the description of the information offered by SDIs. The proposed ontologies describe the semantics of several features of a service, enabling information discovery at level of services, feature types, and geographic data.