Abstract
The capability to perform comparisons of city performances can be an important guide for stakeholders to detect strengths and weaknesses and to set up strategies for future urban development. Today, the rise of the Open Data culture in public administrations is leading to a larger availability of statistical datasets in machine-readable formats, e.g. the RDF Data Cube. Although these allow easier data access and consumption, appropriate evaluation mechanisms are still needed to perform proper comparisons, together with an explicit representation of how statistical indicators are calculated. In this work, we discuss an approach for analysis and comparison of statistical Linked Data which is based on the formal and mathematical representation of performance indicators. Relying on this knowledge model, a set of logic-based services are able to support novel typologies of comparison of different resources.
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Notes
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Full ontology specification is available online at http://w3id.org/kpionto.
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Please note that owl:sameAs links can be defined between different definitions of the same dimension for interoperability purposes.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Diamantini, C., Potena, D., Storti, E. (2018). Comparison of City Performances Through Statistical Linked Data Exploration. In: Longo, A., et al. Cloud Infrastructures, Services, and IoT Systems for Smart Cities. IISSC CN4IoT 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 189. Springer, Cham. https://doi.org/10.1007/978-3-319-67636-4_1
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