Spatial data warehouses (SDW) and spatial online analytical processing (SOLAP) enhance decision making by enabling spatial analysis combined with multidimensional analytical queries. A SDW is an integrated and voluminous multidimensional database containing both conventional and spatial data. SOLAP allows querying SDWs with multidimensional queries that select spatial data that satisfy a given topological relationship and that aggregate spatial data. Existing SDW and SOLAP applications mostly consider phenomena represented by spatial data having exact locations and sharp boundaries. They neglect the fact that spatial data may be affected by imperfections, such as spatial vagueness, which prevents distinguishing an object from its neighborhood. A vague spatial object does not have a precisely defined boundary and/or interior. Thus, it may have a broad boundary and a blurred interior, and is composed of parts that certainly belong to it and parts that possibly belong to it. Although several real-world phenomena are characterized by spatial vagueness, no approach in the literature addresses both spatial vagueness and the design of SDWs nor provides multidimensional analysis over vague spatial data. These shortcomings motivated the elaboration of this doctoral thesis, which addresses both vague spatial data warehouses (vague SDWs) and vague spatial online analytical processing (vague SOLAP). A vague SDW is a SDW that comprises vague spatial data, while vague SOLAP allows querying vague SDWs. The major contributions of this doctoral thesis are: (i) the Vague Spatial Cube (VSCube) conceptual model, which enables the creation of conceptual schemata for vague SDWs using data cubes; (ii) the Vague Spatial MultiDim (VSMultiDim) conceptual model, which enables the creation of conceptual schemata for vague SDWs using diagrams; (iii) guidelines for designing relational schemata and integrity constraints for vague SDWs, and for extending the SQL language to enable vague SOLAP; (iv) the Vague Spatial Bitmap Index (VSB-index), which improves the performance to process queries against vague SDWs. The applicability of these contributions is demonstrated in two applications of the agricultural domain, by creating conceptual schemata for vague SDWs, transforming these conceptual schemata into logical schemata for vague SDWs, and efficiently processing queries over vague SDWs. / Les entrepôts de données spatiales (EDS) et l'analyse en ligne spatiale (ALS) améliorent la prise de décision en permettant l'analyse spatiale combinée avec des requêtes analytiques multidimensionnelles. Un EDS est une base de données multidimensionnelle intégrée et volumineuse qui contient des données classiques et des données spatiales. L'ALS permet l'interrogation des EDS avec des requêtes multidimensionnelles qui sélectionnent des données spatiales qui satisfont une relation topologique donnée et qui agrègent les données spatiales. Les EDS et l'ALS considèrent essentiellement des phénomènes représentés par des données spatiales ayant une localisation exacte et des frontières précises. Ils négligent que les données spatiales peuvent être affectées par des imperfections, comme l'imprécision spatiale, ce qui empêche de distinguer précisément un objet de son entourage. Un objet spatial vague n'a pas de frontière et/ou un intérieur précisément définis. Ainsi, il peut avoir une frontière large et un intérieur flou, et est composé de parties qui lui appartiennent certainement et des parties qui lui appartiennent éventuellement. Bien que plusieurs phénomènes du monde réel sont caractérisés par l'imprécision spatiale, il n'y a pas dans la littérature des approches qui adressent en même temps l'imprécision spatiale et la conception d'EDS ni qui fournissent une analyse multidimensionnelle des données spatiales vagues. Ces lacunes ont motivé l'élaboration de cette thèse de doctorat, qui adresse à la fois les entrepôts de données spatiales vagues (EDS vagues) et l'analyse en ligne spatiale vague (ALS vague). Un EDS vague est un EDS qui comprend des données spatiales vagues, tandis que l'ALS vague permet d'interroger des EDS vagues. Les contributions majeures de cette thèse de doctorat sont: (i) le modèle conceptuel Vague Spatial Cube (VSCube), qui permet la création de schémas conceptuels pour des EDS vagues à l'aide de cubes de données; (ii) le modèle conceptuel Vague Spatial MultiDim (VSMultiDim), qui permet la création de schémas conceptuels pour des EDS vagues à l'aide de diagrammes; (iii) des directives pour la conception de schémas relationnels et des contraintes d'intégrité pour des EDS vagues, et pour l'extension du langage SQL pour permettre l'ALS vague; (iv) l'indice Vague Spatial Bitmap (VSB-index) qui améliore la performance pour traiter les requêtes adressées à des EDS vagues. L'applicabilité de ces contributions est démontrée dans deux applications dans le domaine agricole, en créant des schémas conceptuels des EDS vagues, la transformation de ces schémas conceptuels en schémas logiques pour des EDS vagues, et le traitement efficace des requêtes sur des EDS vagues. / O data warehouse espacial (DWE) é um banco de dados multidimensional integrado e volumoso que armazena dados espaciais e dados convencionais. Já o processamento analítico-espacial online (SOLAP) permite consultar o DWE, tanto pela seleção de dados espaciais que satisfazem um relacionamento topológico, quanto pela agregação dos dados espaciais. Deste modo, DWE e SOLAP beneficiam o suporte a tomada de decisão. As aplicações de DWE e SOLAP abordam majoritarimente fenômenos representados por dados espaciais exatos, ou seja, que assumem localizações e fronteiras bem definidas. Contudo, tais aplicações negligenciam dados espaciais afetados por imperfeições, tais como a vagueza espacial, a qual interfere na identificação precisa de um objeto e de seus vizinhos. Um objeto espacial vago não tem sua fronteira ou seu interior precisamente definidos. Além disso, é composto por partes que certamente pertencem a ele e partes que possivelmente pertencem a ele. Apesar de inúmeros fenômenos do mundo real serem caracterizados pela vagueza espacial, na literatura consultada não se identificaram trabalhos que considerassem a vagueza espacial no projeto de DWE e nem para consultar o DWE. Tal limitação motivou a elaboração desta tese de doutorado, a qual introduz os conceitos de DWE vago e de SOLAP vago. Um DWE vago é um DWE que armazena dados espaciais vagos, enquanto que SOLAP vago provê os meios para consultar o DWE vago. Nesta tese, o projeto de DWE vago é abordado e as principais contribuições providas são: (i) o modelo conceitual VSCube que viabiliza a criação de um cubos de dados multidimensional para representar o esquema conceitual de um DWE vago; (ii) o modelo conceitual VSMultiDim que permite criar um diagrama para representar o esquema conceitual de um DWE vago; (iii) diretrizes para o projeto lógico do DWE vago e de suas restrições de integridade, e para estender a linguagem SQL visando processar as consultas de SOLAP vago no DWE vago; e (iv) o índice VSB-index que aprimora o desempenho do processamento de consultas no DWE vago. A aplicabilidade dessas contribuições é demonstrada em dois estudos de caso no domínio da agricultura, por meio da criação de esquemas conceituais de DWE vago, da transformação dos esquemas conceituais em esquemas lógicos de DWE vago, e do processamento de consultas envolvendo as regiões vagas do DWE vago. / Doctorat en Sciences de l'ingénieur et technologie / Location of the public defense: Universidade Federal de São Carlos, São Carlos, SP, Brazil. / info:eu-repo/semantics/nonPublished

Les systèmes Spatial On-Line Analytical Processing (SOLAP) permettent de prendre en charge l’analyse multidimensionnelle en ligne d’un très grand volume de données ayant une référence spatiale. Dans ces systèmes, le vague spatial n’est généralement pas pris en compte, ce qui peut être source d’erreurs dans les analyses et les interprétations des cubes de données SOLAP, effectuées par les utilisateurs finaux. Bien qu’il existe des modèles d’objets ad-hoc pour gérer le vague spatial, l’implantation de ces modèles dans les systèmes SOLAP est encore à l’état embryonnaire. En outre, l’introduction de tels modèles dans les systèmes SOLAP accroit la complexité de l’analyse au détriment de l’utilisabilité dans bon nombre de contextes applicatifs. Dans cette thèse nous nous proposons d’investiguer la piste d’une nouvelle approche visant un compromis approprié entre l’exactitude théorique de la réponse au vague spatial, la facilité d’implantation dans les systèmes SOLAP existants et l’utilisabilité des cubes de données fournis aux utilisateurs finaux.Les objectifs de cette thèse sont donc de jeter les bases d’une approche de conception de cube SOLAP où la gestion du vague est remplacée par la gestion des risques de mauvaises interprétations induits, d’en définir les principes d’une implantation pratique et d’en démontrer les avantages.En résultats aux travaux menés, une approche de conception de cubes SOLAP où le risque de mauvaise interprétation est considéré et géré de manière itérative et en adéquation avec les sensibilités des utilisateurs finaux quant aux risques potentiels identifiés a été proposée; des outils formels à savoir un profil UML adapté, des fonctions de modification de schémas multidimensionnels pour construire les cubes souhaités, et un processus formel guidant de telles transformations de schémas ont été présentés; la vérification de la faisabilité de notre approche dans un cadre purement informatique avec la mise en oeuvre de l’approche dans un outil CASE (Computed Aided Software Engineering) a aussi été présentée. Pour finir, nous avons pu valider le fait que l’approche fournisse non seulement des cubes aussi compréhensibles et donc utilisables que les cubes classiques, mais aussi des cubes où le vague n’est plus laissé de côté, sans aucun effort pour atténuer ses impacts sur les analyses et les prises de décision des utilisateurs finaux. / SOLAP (Spatial On-Line Analytical Processing) systems support the online multi-dimensional analysis of a very large volume of data with a spatial reference. In these systems, the spatial vagueness is usually not taken into account, which can lead to errors in the SOLAP datacubes analyzes and interpretations end-users make. Although there are ad-hoc models of vague objects to manage the spatial vagueness, the implementation of these models in SOLAP systems is still in an embryonal state. In addition, the introduction of such models in SOLAP systems increases the complexity of the analysis at the expense of usability in many application contexts. In this thesis we propose to investigate the trail of a new approach that makes an appropriate compromise between the theoretical accuracy of the response to the spatial vagueness, the ease of implementation in existing SOLAP systems and the usability of datacubes provided to end users.The objectives of this thesis are to lay the foundations of a SOLAP datacube design approach where spatial vagueness management in itself is replaced by the management of risks of misinterpretations induced by the vagueness, to define the principles of a practical implementation of the approach and to demonstrate its benefits.The results of this thesis consist of a SOLAP datacube design approach where the risks of misinterpretation are considered and managed in an iterative manner and in line with the end users tolerance levels regarding those risks; formal tools namely a suitable UML (Unified Modeling Language) profile, multidimensional schemas transformation functions to help tailored the datacubes to end-users tolerance levels, and a formal process guiding such schemas transformation; verifying the feasibility of our approach in a computing context with the implementation of the approach in a CASE (Computed Aided Software Engineering) tool. Finally, we were able to validate that the approach provides SOLAP datacubes that are not only as comprehensible and thus usable as conventional datacubes but also datacubes where the spatial vagueness is not left out, with no effort to mitigate its impacts on analysis and decision making for end users.

Made available in DSpace on 2016-06-02T19:04:00Z (GMT). No. of bitstreams: 1 6824.pdf: 22060515 bytes, checksum: bde19feb7a6e296214aebe081f2d09de (MD5) Previous issue date: 2015-12-07 / Universidade Federal de Minas Gerais / O data warehouse espacial (DWE) é um banco de dados multidimensional integrado e volumoso que armazena dados espaciais e dados convencionais. Já o processamento analítico espacial online (SOLAP) permite consultar o DWE, tanto pela seleção de dados espaciais que satisfazem um relacionamento topológico, quanto pela agregação dos dados espaciais. Deste modo, DWE e SOLAP beneficiam o suporte a tomada de decisão. As aplicações de DWE e SOLAP abordam majoritarimente fenômenos representados por dados espaciais exatos, ou seja, que assumem localizações e fronteiras bem definidas. Contudo, tais aplicações negligenciam dados espaciais afetados por imperfeições, tais como a vagueza espacial, a qual interfere na identificação precisa de um objeto e de seus vizinhos. Um objeto espacial vago não tem sua fronteira ou seu interior precisamente definidos. Além disso, é composto por partes que certamente pertencem a ele e partes que possivelmente pertencem a ele. Apesar de inúmeros fenômenos do mundo real serem caracterizados pela vagueza espacial, na literatura consultada não se identificaram trabalhos que considerassem a vagueza espacial no projeto de DWE e nem para consultar o DWE. Tal limitação motivou a elaboração desta tese de doutorado, a qual introduz os conceitos de DWE vago e de SOLAP vago. Um DWE vago é um DWE que armazena dados espaciais vagos, enquanto que SOLAP vago provê os meios para consultar o DWE vago. Nesta tese, o projeto de DWE vago é abordado e as principais contribuições providas são: (i) o modelo conceitual VSCube que viabiliza a criação de um cubos de dados multidimensional para representar o esquema conceitual de um DWE vago; (ii) o modelo conceitual VSMultiDim que permite criar um diagrama para representar o esquema conceitual de um DWE vago; (iii) diretrizes para o projeto lógico do DWE vago e de suas restrições de integridade, e para estender a linguagem SQL visando processar as consultas de SOLAP vago no DWE vago; e (iv) o índice VSB-index que aprimora o desempenho do processamento de consultas no DWE vago. A aplicabilidade dessas contribuições é demonstrada em dois estudos de caso no domínio da agricultura, por meio da criação de esquemas conceituais de DWE vago, da transformação dos esquemas conceituais em esquemas lógicos de DWE vago, e do processamento de consultas envolvendo as regiões vagas do DWE vago. / Spatial data warehouses (SDW) and spatial online analytical processing (SOLAP) enhance decision making by enabling spatial analysis combined with multidimensional analytical queries. A SDW is an integrated and voluminous multidimensional database containing both conventional and spatial data. SOLAP allows querying SDWs with multidimensional queries that select spatial data that satisfy a given topological relationship and that aggregate spatial data. Existing SDW and SOLAP applications mostly consider phenomena represented by spatial data having exact locations and sharp boundaries. They neglect the fact that spatial data may be affected by imperfections, such as spatial vagueness, which prevents distinguishing an object from its neighborhood. A vague spatial object does not have a precisely defined boundary and/or interior. Thus, it may have a broad boundary and a blurred interior, and is composed of parts that certainly belong to it and parts that possibly belong to it. Although several real-world phenomena are characterized by spatial vagueness, no approach in the literature addresses both spatial vagueness and the design of SDWs nor provides multidimensional analysis over vague spatial data. These shortcomings motivated the elaboration of this doctoral thesis, which addresses both vague spatial data warehouses (vague SDWs) and vague spatial online analytical processing (vague SOLAP). A vague SDW is a SDW that comprises vague spatial data, while vague SOLAP allows querying vague SDWs. The major contributions of this doctoral thesis are: (i) the Vague Spatial Cube (VSCube) conceptual model, which enables the creation of conceptual schemata for vague SDWs using data cubes; (ii) the Vague Spatial MultiDim (VSMultiDim) conceptual model, which enables the creation of conceptual schemata for vague SDWs using diagrams; (iii) guidelines for designing relational schemata and integrity constraints for vague SDWs, and for extending the SQL language to enable vague SOLAP; (iv) the Vague Spatial Bitmap Index (VSB-index), which improves the performance to process queries against vague SDWs. The applicability of these contributions is demonstrated in two applications of the agricultural domain, by creating conceptual schemata for vague SDWs, transforming these conceptual schemata into logical schemata for vague SDWs, and efficiently processing queries over vague SDWs.