Abstract
Cities are alive: they rise, grow, evolve like living beings. The state of a city changes continuously, influenced by a lot of factors, both human (people moving in the city or extending it) and natural ones (rain or climate changes). Cities are potentially huge sources of data of any kind and for the last years a lot of effort has been put in order to create and extract those sources. This scenario offers a lot of opportunities for mashup developers: by combining and processing the huge amount of data (both public and private) is possible to create new services for urban stakeholders—citizens, tourists, etc. In this chapter, we illustrate the challenges in developing mashups for the urban environments: starting out from the specificity of cities and the availability of urban data and services, we describe a number of scenarios for urban mashups, we present our experience in realizing demonstrators of urban mashups and we discuss the lesson learned and the implications for citizens, tourists and municipalities.
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Notes
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Cf. http://www.waze.com.
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Cf. http://amat-mi.it.
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Cf. http://www.ilmeteo.it (Italian).
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Those rules are both manually coded and generated by machine learning algorithms with specific reference to the Korean language.
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The amount of data available in June 2010, when the mashup was realized.
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For the sake of clarity we assume that the Google map representation is correct.
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It is an assumption for the data that do not change or change very slowly—city topography and calendars.
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Acknowledgements
This research was partially funded by the EU LarKC Project (FP7-215535). We would like to thank the project partners for their collaboration and in particular: Stefano Ceri, Tony Lee, Volker Tresp and Frank van Harmelen.
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Dell’Aglio, D., Celino, I., Della Valle, E. (2013). Urban Mashups. In: Endres-Niggemeyer, B. (eds) Semantic Mashups. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36403-7_10
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