June 5, 2015: Professor Isabel Cruz gives a keynote talk at the 18th AGILE International Conference on Geographic Information Science

Professor Isabel Cruz gives a keynote talk at the 18th AGILE International Conference on Geographic Information Science. The keynote title and abstract are as follows:

Big Data Integration to Enable Citizen Participation in Smart Cities


To enable citizen participation in their city's life and governance we are developing a framework to establish interconnections among big data and allow the public to access those interconnections on an interactive web visualization platform, called GIVA (for Geospatial and temporal data Integration, Visualization, and Analytics). Using this framework, users can select regions in a map, specify time intervals, and select datasets to produce reports where values pertaining to different datasets are compared, analyzed, and visualized. The ultimate goal is for policy evaluation to be at everybody's fingertips. For example, citizens would be able to analyze the consequences of the planned closing of 50 Chicago public schools once data become available (in multiple data formats and resolutions) for those school districts. Relevant data may include education outcomes, crime statistics, property values, economic activity, and income distribution. Data are available from a variety of web sources in the form of tables. Therefore, an important component of GIVA is the ability to extract automatically data from tables. In fact, there are millions of web tables with geographic (and temporal) data that are pertinent not only to policy studies, but also to many other domains, such as urban sustainability, transportation networks, and public health. These tables are highly heterogeneous in structure, concepts, and metadata. One of the challenges in semantically extracting data from tables is the resolution of heterogeneities so as to uncover a conceptual hierarchy, metadata associated with the instances, and cell values, including geolocations. Once these important steps are achieved, big data integration can become a reality.

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