A UML Profile for Variety and Variability Awareness in Multidimensional Design: An application to Agricultural Robots

Variety and variability are an inherent source of information wealth in schemaless sources, and executing OLAP sessions on multidimensional data in their presence has recently become an object of research. However, all models devised so far propose a ``rigid'' view of the multidimensional content, without taking into account variety and variability. To fill this gap, in this paper we propose V-ICSOLAP, an extension of the ICSOLAP UML profile that supports extensibility and type/name variability for each multidimensional element, as well as complex data types for measures and levels. The real case study we use to motivate and illustrate our approach is that of trajectory analysis for agricultural robots. As a proof-of-concept for V-ICSOLAP, we propose an implementation that relies on the PostgreSQL multi-model DBMS and we evaluate its performances. We also provide a validation of our UML profile by ranking it against other meta-models based on a set of quality metrics

The representation and management of evolving features in geospatial databases

Geographic features change over time, this change being the result of some kind of event or occurrence. It has been a research challenge to represent this data in a manner that reflects human perception. Most database systems used in geographic information systems (GIS) are relational, and change is either captured by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent diffculty of modelling geographic objects in relational tables. This diffculty is compounded when the necessary time dimension is introduced to model how those objects evolve. There is little doubt that the object-oriented (OO) paradigm holds signi cant advantages over the relational model when it comes to modelling real-world entities and spatial data, and it is argued that this contention is particularly true when it comes to spatio-temporal data. This thesis describes an object-oriented approach to the design of a conceptual model for representing spatio-temporal geographic data, called the Feature Evolution Model (FEM), based on states and events. The model was used to implement a spatio-temporal database management system in Oracle Spatial, and an interface prototype is described that was used to evaluate the system by enabling querying and visualisation

GDMS-R: A mixed SQL to manage raster and vector data

11pInternational audienceTo evaluate urbanization impact on territories, an accurate knowledge of the urban and peri-urban fabrics is unavoidable. To provide advanced characterization of the terrain, modern GIS applications target even wider geographic areas at finer resolutions but they also have to mix data of different types such as Digital Elevation Model (raster layer), buildings (polygonal layer) and roads (polylines layer). Processing both raster and vector data with the same semantic and in an efficient way presents significant challenges to GIS insofar as underlying granularities but also data layout and processing patterns might be absolutely different. We have already focused on the definition and the implementation of an abstraction layer called GDMS (Generic Datasource Management System) to handle and process vector data. Main objectives with GDMS, were to provide the user not only a simple and powerful API but also a spatial SQL derived language. Moreover, as an intermediate layer between the user and the information source, GDMS intends to reduce the coupling between the processes and the specificities of each underlying format. As a consequence, former work may easily be reused in a much larger set of scenarii. The learning curve is consequently even simpler. In this paper, we propose a raster extension to the GDMS layer called GDMS-R. Even if, there is currently no OGC standard concerning raster processing (using well-known SQL language), there already exists a de facto standard called Map Algebra defined by C. D. Tomlin in 1990 and commonly implemented in a wide set of GIS. Our objective is a bit different insofar as we propose to extend SQL language. We present the integration of Map Algebra concepts in GDMS through the GRAP (GeoRAster Processing) language. As for GDMS, reuse is enhanced by the possibility of being vendor-independent (middle-ware approach) and the extension capabilities of the underlying SQL language. To demonstrate the capabilities of GDMS-R, we present a use case relative to the deep impact of increased urbanization on the vulnerability of peri-urban hydro-systems: impact of the linear constraints on the runoff water pathways and accumulation that uses both vector and raster data in an unified way

Geospatial Data Management Research: Progress and Future Directions

Without geospatial data management, today´s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis

Mastering the Spatio-Temporal Knowledge Discovery Process

The thesis addresses a topic of great importance: a framework for data mining positioning data collected by personal mobile devices. The main contribution of this thesis is the creation of a theoretical and practical framework in order to manage the complex Knowledge discovery process on mobility data. Hence the creation of such framework leads to the integration of very different aspects of the process with their assumptions and requirements. The result is a homogeneous system which gives the possibility to exploit the power of all the components with the same flexibilities of a database such as a new way to use the ontology for an automatic reasoning on trajectory data. Furthermore two extensions are invented and developed and then integrated in the system to confirm the extensibility of it: a innovative way to reconstruct the trajectories considering the uncertainty of the path followed and a Location prediction algorithm called WhereNext. Another important contribution of the thesis is the experimentation on a real case of study on analysis of mobility data. It has been shown the usefulness of the system for a mobility manager who is provided with a knowledge discovery framework

A framework for the management of deformable moving objects

There is an emergence of a growing number of applications and services based on spatiotemporal data in the most diverse areas of knowledge and human activity. The Internet of Things (IoT), the emergence of technologies that make it possible to collect information about the evolution of real world phenomena and the widespread use of devices that can use the Global Positioning System (GPS), such as smartphones and navigation systems, suggest that the volume and value of these data will increase significantly in the future. It is necessary to develop tools capable of extracting knowledge from these data and for this it is necessary to manage them: represent, manipulate, analyze and store, in an efficient way. But this data can be complex, its management is not trivial and there is not yet a complete system capable of performing this task. Works on moving points, that represent the position of objects over time, are frequent in the literature. On the contrary there are much less solutions for the representation of moving regions, that represent the continuous changes in position, shape and extent of objects over time, e.g., storms, fires and icebergs. The representation of the evolution of moving regions is complex and requires the use of more elaborate techniques, e.g., morphing and interpolation techniques, capable of producing realistic and geometrically valid representations. In this dissertation we present and propose a data model for moving objects (moving points and moving regions), in particular for moving regions, based on the concept of mesh and compatible triangulation and rigid interpolation methods. This model was implemented in a framework that is not client or application dependent and we also implemented a spatiotemporal extension for PostgreSQL that uses this framework to manipulate and analyze moving objects, as a proof of concept that our framework works with real applications. The tests’ results using real data, obtained from satellite images of the evolution of 2 icebergs over time, show that our data model works. Besides the results obtained one important contribution of this work is the development of a basic framework for moving objects that can be used as a basis for further investigation in this area. A few problems still remain that must be further studied and analyzed, in particular, the ones that were found when using the compatible triangulation and rigid interpolation methods with real data.Assistimos ao aparecimento de um número crescente de aplicações e serviços baseados em dados espácio-temporais nas mais diversas áreas do conhecimento e da atividade humana. A internet das coisas (IoT), o aparecimento de novas tecnologias que permitem obter dados sobre a evolução de fenómenos do mundo real e o uso generalizado de dispositivos que usam o sistema de posicionamento global (GPS), por exemplo, smartphones e sistemas de navegação, sugerem que o volume e o valor destes dados aumente significativamente no futuro. Torna-se necessário desenvolver ferramentas capazes de extrair conhecimento destes dados e para isso é necessário geri-los: representar, manipular, analisar e armazenar, de uma forma eficiente. Mas estes dados podem ser complexos, a sua gestão não é trivial e ainda não existe um sistema completo capaz de executar essa tarefa. Existe muito trabalho na literatura sobre pontos móveis, que representam as alterações da posição de objectos ao longo do tempo, mas existe muito menos trabalho realizado sobre regiões móveis, que representam as alterações da posição e da forma de regiões ao longo do tempo, por exemplo, uma tempestade, um incêndio ou um derramamento de petroleo. A representação da evolução de regiões móveis ao longo do tempo é complexa e exige o uso de técnicas mais elaboradas, por exemplo, técnicas de morphing e interpolação, capazes de produzir representações realistas e geometricamente válidas. Nesta dissertação apresentamos e propomos um modelo de dados para trabalhar com objetos móveis (pontos móveis e regiões móveis), em particular regiões móveis, baseado no conceito de malha e em métodos de triangulação compatível e interpolação rígida. Este modelo foi implementado num framework que é independente do cliente e da aplicação. Também implementámos uma extensão espácio-temporal para o sistema de gestão de base de dados PostgreSQL, que usa este framework para manipular e analisar objectos móveis, como uma prova de conceito que o nosso framework funciona com aplicações reais. Os resultados dos testes com dados reais, obtidos a partir de imagens de satélite da evolução de 2 icebergs ao longo do tempo, demonstram que o nosso modelo funciona. Para além dos resultados obtidos, um contributo importante desta dissertação é o desenvolvimento de um framework que pode ser usado como a base para trabalho futuro e investigação nesta área. Existem alguns problemas ainda por resolver e que devem ser analisados e estudados com mais cuidado, em particular, os que foram encontrados quando usámos os métodos de triangulação compatível e interpolação rigída em dados reais.Mestrado em Engenharia Informátic

A Framework to Support Spatial, Temporal and Thematic Analytics over Semantic Web Data

Spatial and temporal data are critical components in many applications. This is especially true in analytical applications ranging from scientific discovery to national security and criminal investigation. The analytical process often requires uncovering and analyzing complex thematic relationships between disparate people, places and events. Fundamentally new query operators based on the graph structure of Semantic Web data models, such as semantic associations, are proving useful for this purpose. However, these analysis mechanisms are primarily intended for thematic relationships. In this paper, we describe a framework built around the RDF data model for analysis of thematic, spatial and temporal relationships between named entities. We present a spatiotemporal modeling approach that uses an upper-level ontology in combination with temporal RDF graphs. A set of query operators that use graph patterns to specify a form of context are formally defined. We also describe an efficient implementation of the framework in Oracle DBMS and demonstrate the scalability of our approach with a performance study using both synthetic and real-world RDF datasets of over 25 million triple