Daniel Mejia.
Abstract: The core of Smart Cities solutions, at the research and application level is the use of technology to improve quality of life of city residents. Measuring the improvement that specific solutions bring normally requires data collection and analytics before and after the implementation of such solutions. This work involves traffic incidents, where data is available for use, but there is a lack of a comprehensive understanding on safety and efficiency metrics. We believe that methodologies for modeling and evaluating semantically annotated data is the driving factor for understanding real world situations in a city. By using a data-driven ontology, it is expected that new information can be revealed and used. Data-driven ontologies can enable the creation of metrics for use by a wide variety of stakeholders, from do-main experts to city residents. This work focuses on the creation of semantically annotated data-driven indicators that are maintainable, changeable, and transferable amongst cities with similar data.
Keywords: Smart Cities; Semantically Annotated Data-Driven Modeling; Ontology; Interdisciplinary Research; Linked Data