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public:research [2016/09/08 02:23] clodoveu |
public:research [2016/09/08 22:35] clodoveu [Geographic Data Modeling] |
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| ==== Geographic Data Modeling ==== | ==== Geographic Data Modeling ==== | ||
| - | Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque convallis mauris eget nisl bibendum elementum. Morbi et dui magna. Duis pretium turpis sed lectus consequat, in porttitor libero pulvinar. Proin consequat urna eget molestie finibus. Curabitur pretium porta mi, at laoreet libero venenatis sit amet. Cras congue nisl ac lobortis congue. Vestibulum nulla velit, auctor sit amet rutrum eu, congue at nulla. | + | The first data models developed for geographic applications were guided by existing GIS internal structures, forcing the user to adjust his/her interpretation of spatial phenomena to whatever structures were available. As a consequence, the modeling process did not offer mechanisms that would allow for the representation of the reality according to the user's mental model. Even well-known semantic and object-oriented data models, such as the Entity-Relationship (ER) model [11], the Object Modeling Technique (OMT) model [39], and the IFO model [1], do not offer adequate facilities to represent geographic applications. Even though these models are highly expressive, they present limitations to the adequate modeling of such applications, since they do not include geographic primitives that would allow for a satisfactory representation of spatial data. |
| + | Considering these limitations, OMT-G was proposed in 2001, and evolved steadily since. OMT-G is a data model for the design of geographic database systems and applications. OMT-G starts with Unified Modeling Language (UML) class diagram primitives, introducing geographic primitives in order to enhance UML's semantic representation capabilities, thus reducing the distance between the designer's mental model of the reality and the usual representation tools. OMT-G provides primitives for modeling the geometric shape and location of geographic objects, supporting spatial and topological relationships, “whole-part” structures, networks, and multiple representations. Furthermore, the model allows the specification of alphanumeric attributes and methods associated to each class. The model's main strong points include its graphical expressivity and its compactness, since textual annotations are replaced by pictograms and symbols indicating explicit relationships, which are able to denote the dynamic nature of the interaction between spatial and non-spatial objects. From the model, it is also possible to derive spatial integrity constraints, specified along with the usual constraints found in conventional database design. Using these assets, the mapping between the conceptual schema and the physical implementation can be executed more soundly and preserving the semantics contained in the higher abstraction level. | ||
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| + | OMT-G is the basis for our research on geographic data modeling. We are interested in various related themes, including modeling tools and techniques, spatial integrity constraints, automatic mapping to physical object-relational implementations and mapping to NoSQL database managers. | ||
| ==== Geographic Information Retrieval ==== | ==== Geographic Information Retrieval ==== | ||
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| ==== Urban Computing ==== | ==== Urban Computing ==== | ||
| - | Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque convallis mauris eget nisl bibendum elementum. Morbi et dui magna. Duis pretium turpis sed lectus consequat, in porttitor libero pulvinar. Proin consequat urna eget molestie finibus. Curabitur pretium porta mi, at laoreet libero venenatis sit amet. Cras congue nisl ac lobortis congue. Vestibulum nulla velit, auctor sit amet rutrum eu, congue at nulla. | + | The expression //Urban Computing// designates the process of acquiring, integrating and analyzing large volumes of heterogeneous data, generated by various sources in the urban space. These sources range from environmental sensors to official governmental data, and include the direct participation of citizens in crowdsourcing or volunteered information initiatives. Data and information managed in this process are directed to the understanding and solution of urban problems that are typical of large cities in Brazil and abroad, such as mobility, public health, air and sound pollution, water and energy consumption, and many others. There is a three-fold concern: on improving the urban environment for human (co)existence, on improving urban quality of living, and on improving the conditions for the operation, by governmental authorities and public utility companies, of the various systems that comprise the city. Our objectives in Urban Computing research is to establish a qualified cycle for collection, integration and use of geographic information to the benefit of society, fostering the evolution of the state-of-the-art in topics along this cycle, such as spatial data infrastructures. Research outcomes are applied to typical urban problems, with an emphasis on the use of geographic location as a factor for data integration and for communicating findings, as feedback to the society. |
public/research.txt · Last modified: 2016/09/08 23:09 by clodoveu
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