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public:research [2016/09/08 02:05]
clodoveu
public:research [2016/09/08 22:36]
clodoveu
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 The volume and variety of geographic data available on the Web for the common citizen are increasing rapidly. Since the onset of Web 2.0, there is much interest on tools that allow people to geographically locate and describe aspects of their daily life, and to share such knowledge with other people. Initial applications show that it is possible to mobilize the interest of large numbers of citizens for the creation, dissemination and maintenance of geographic information on socially relevant themes. This line of research focuses on the design and implementation of computational tools and techniques that allow groups of people to act as human sensors, voluntarily (or unconsciously) contributing information for the common good. The research agenda includes investigating user motivation, data quality, user feedback and spatial coverage of contributions. We also work on methods for active and passive crowdsourcing and crowdsensing,​ seeking the application of recommendation systems to enhance volunteered contributions. The volume and variety of geographic data available on the Web for the common citizen are increasing rapidly. Since the onset of Web 2.0, there is much interest on tools that allow people to geographically locate and describe aspects of their daily life, and to share such knowledge with other people. Initial applications show that it is possible to mobilize the interest of large numbers of citizens for the creation, dissemination and maintenance of geographic information on socially relevant themes. This line of research focuses on the design and implementation of computational tools and techniques that allow groups of people to act as human sensors, voluntarily (or unconsciously) contributing information for the common good. The research agenda includes investigating user motivation, data quality, user feedback and spatial coverage of contributions. We also work on methods for active and passive crowdsourcing and crowdsensing,​ seeking the application of recommendation systems to enhance volunteered contributions.
  
-==== Spatial Databases ====+==== Spatial Databases ​and Geographic Data Modeling ​====
  
-Lorem ipsum dolor sit ametconsectetur adipiscing elitQuisque convallis mauris eget nisl bibendum elementum. Morbi et dui magna. Duis pretium turpis sed lectus consequatin porttitor libero pulvinarProin consequat urna eget molestie finibus. Curabitur pretium porta miat laoreet libero venenatis sit amet. Cras congue nisl ac lobortis congueVestibulum nulla velitauctor sit amet rutrum eucongue at nulla.+The first data models developed for geographic applications were guided by existing GIS internal structuresforcing the user to adjust his/her interpretation of spatial phenomena to whatever structures were availableAs a consequencethe modeling process did not offer mechanisms that would allow for the representation of the reality according to the user's mental modelEven well-known semantic and object-oriented data modelssuch 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 applicationsEven though these models are highly expressivethey present limitations to the adequate modeling of such applicationssince they do not include geographic primitives that would allow for a satisfactory representation of spatial data.
  
-==== Geographic Data Modeling ​====+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.
  
-Lorem ipsum dolor sit amet, consectetur adipiscing elitQuisque 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 miat laoreet libero venenatis sit amet. Cras congue nisl ac lobortis congue. Vestibulum nulla velitauctor sit amet rutrum eucongue at nulla.+OMT-G is the basis for our research on geographic data modelingWe are interested ​in various related themesincluding modeling tools and techniquesspatial integrity constraintsautomatic mapping to physical object-relational implementations and mapping to NoSQL database managers.
  
 ==== Geographic Information Retrieval ==== ==== Geographic Information Retrieval ====
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 A large share of the information available on the Web is geographically specific. References to geographic locations appear in the form of place names, postal addresses, postcodes, historical dates, demonyms, ethnicity, typical food and others. Many queries include place names and other geographic terms. Therefore, there is demand for mechanisms to search for documents both thematically (for instance, using a set of keywords) and geographically,​ based on places mentioned or referenced by the text. Similar techniques and resources can also apply to streaming data, such as Twitter messages or RSS feeds, providing the opportunity to index content in near-real-time,​ based on references to places. A large share of the information available on the Web is geographically specific. References to geographic locations appear in the form of place names, postal addresses, postcodes, historical dates, demonyms, ethnicity, typical food and others. Many queries include place names and other geographic terms. Therefore, there is demand for mechanisms to search for documents both thematically (for instance, using a set of keywords) and geographically,​ based on places mentioned or referenced by the text. Similar techniques and resources can also apply to streaming data, such as Twitter messages or RSS feeds, providing the opportunity to index content in near-real-time,​ based on references to places.
  
-However, while finding references to places in Web documents, ambiguity and uncertainty occur. Places can share a name with other places (Paris, besides being the capital of France, refers to more than sixty places around the world. Places are named using common language words (Park, Hope and Independence are American cities) and proper names (Washington,​ Houston and San Francisco). ​The first type of ambiguity occurs when a place name references multiple places, and it is called *Geo/Geo ambiguity*. The latter ambiguity is called *Geo/​Non-Geo ambiguity*, which occurs when both a location and a non-location share the same name. There is a third type of ambiguity, named *reference ambiguity*which occurs when a place is associated to many names, like New York, NYC or The Big Apple. Ambiguity makes the resolution of references to places intrinsically context-based. Although there are important work on place-based information integration and retrieval, areas such as disambiguation are still in its infancy.+However, while finding references to places in Web documents, ambiguity and uncertainty occur. Places can share a name with other places (Paris, besides being the capital of France, refers to more than sixty places around the world. Places are named using common language words (Park, Hope and Independence are American cities) and proper names (Washington,​ Houston and San Francisco). ​Also, a place can be associated to many names, like New York, NYC or The Big Apple, and to names in various languages. Ambiguity makes the resolution of references to places intrinsically context-based. Although there are important work on place-based information integration and retrieval, areas such as disambiguation are still in their infancy.
  
-References to places can be straightforward and unambiguous as geographic coordinates or not. Other sources of geographic location information can be structured (postal addresses) or unstructured (place descriptions in text). They can also be direct (place names) or indirect (references to cultural characteristics associated to places), explicit (news headers) or implicit ("​9/​11"​). Humans are often able to recognize references to places based on such evidence, but this association does not come so easily to automated systems. Addressing this problem is one of the pressing ​tasks for Geographic Information Retrieval (GIR) research.+References to places can be straightforward and unambiguous as geographic coordinates or not. Other sources of geographic location information can be structured (postal addresses) or unstructured (place descriptions in text). They can also be direct (place names) or indirect (references to cultural characteristics associated to places), explicit (news headers) or implicit ("​9/​11"​). Humans are often able to recognize references to places based on such evidence, but this association does not come so easily to automated systems. Addressing this problem is one of the tasks for Geographic Information Retrieval (GIR) research.
  
 GIR extends Information Retrieval with geographic locations and metadata, taking it beyond the use of keywords. ​ GIR extends Information Retrieval with geographic locations and metadata, taking it beyond the use of keywords. ​
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 ==== Urban Computing ==== ==== Urban Computing ====
  
-Lorem ipsum dolor sit ametconsectetur adipiscing elit. Quisque convallis mauris eget nisl bibendum elementumMorbi et dui magna. Duis pretium turpis sed lectus consequat, in porttitor libero pulvinarProin consequat urna eget molestie finibusCurabitur pretium porta miat laoreet libero venenatis sit ametCras congue nisl ac lobortis congueVestibulum nulla velitauctor sit amet rutrum eucongue at nulla.+The expression //Urban Computing// designates the process of acquiringintegrating and analyzing large volumes of heterogeneous data, generated by various sources in the urban spaceThese sources range from environmental sensors to official governmental dataand include the direct participation of citizens ​in crowdsourcing or volunteered information initiativesData 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 othersThere is a three-fold concern: on improving the urban environment for human (co)existenceon 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 cityOur 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 infrastructuresResearch outcomes are applied to typical urban problemswith an emphasis on the use of geographic location as a factor for data integration and for communicating findingsas feedback to the society. 
public/research.txt · Last modified: 2016/09/08 23:09 by clodoveu