Lab CS+X

Laboratory of Interdisciplinary Computer Science

User Tools

Site Tools


public:research

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Next revision
Previous revision
Last revision Both sides next revision
public:research [2016/04/28 14:22]
lizardo created
public:research [2016/09/08 22:36]
clodoveu
Line 7: Line 7:
 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. 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.
  
-==== Spatial ​Databases ​====+==== Spatial ​Data Infrastructures ​====
  
-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 ametCras congue nisl ac lobortis congueVestibulum nulla velitauctor sit amet rutrum eu, congue at nulla.+Spatial Data Infrastructures (SDI) are a new approach for the creationdistribution and use of geographic information,​ with an emphasis on interoperabilitySDIs seek to go beyond the simple distribution of previously existing maps and cartographic datato serve as data sources powered by standardized Web servicesSDIs have the potential to become fundamental elements for understanding spacedisseminating geographic data and information along with metadata on provenance, quality and semanticsThe typical SDI user is someone who needs to combine and integrate data from various sources to generate new insights on a field of study or applicationIn this perspectiveSDIs can have a central role in areas such as environmental management and urban planning.
  
-==== Geographic ​Data Modeling ​====+==== Volunteered ​Geographic ​Information ​====
  
-Lorem ipsum dolor sit ametconsectetur adipiscing elitQuisque convallis mauris eget nisl bibendum elementumMorbi et dui magnaDuis 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 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 lifeand to share such knowledge with other peopleInitial 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 themesThis 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 goodThe research agenda includes investigating user motivationdata 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 and Geographic Data Modeling ==== 
 + 
 +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 sinceOMT-G is a data model for the design of geographic database systems and applicationsOMT-G starts with Unified Modeling Language (UML) class diagram primitivesintroducing 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 toolsOMT-G provides primitives for modeling the geometric shape and location of geographic objects, supporting spatial and topological relationships,​ “whole-part” structures, networks, and multiple representationsFurthermore,​ 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. 
 + 
 +OMT-G is the basis for our research on geographic data modeling. We are interested in various related themes, including modeling tools and techniquesspatial integrity constraintsautomatic mapping to physical object-relational implementations and mapping to NoSQL database managers.
  
 ==== Geographic Information Retrieval ==== ==== Geographic Information Retrieval ====
  
-Lorem ipsum dolor sit ametconsectetur adipiscing elitQuisque convallis mauris eget nisl bibendum elementumMorbi et dui magnaDuis 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 demand for geographic data in applications on the Web is increasing. One of the most important resources to support this increased interest is the ability to recognize references to places in Web documents. If documents can be correctly and efficiently linked to places mentioned directly or indirectly in themit becomes possible to improve and innovate in directions such as geographic indexing and querying, finding relationships based on spatial proximity or containment,​ and detecting localized trends for events and phenomena mentioned in social media. 
 + 
 +A large share of the information available on the Web is geographically specificReferences to geographic locations appear in the form of place names, postal addresses, postcodes, historical dates, demonyms, ethnicity, typical food and othersMany queries include place names and other geographic terms. Thereforethere 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 occurPlaces can share a name with other places (Parisbesides being the capital of France, refers to more than sixty places around the worldPlaces 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 tasks for Geographic Information Retrieval (GIR) research. 
 + 
 +GIR extends Information Retrieval with geographic locations and metadata, taking it beyond the use of keywords 
 +GIR studies methods and techniques for the retrieval of information from unstructured or partially structured sourcesincluding relevance rankingbased on queries that specify both theme and geographic scope
  
 ==== 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