Abstract
Nowadays, we are witnessing a rapid increase of spatio-temporal data that permeates different aspects of our everyday life such as mobile geolocation services and geo-located weather sensors. This big amount of data needs innovative analytics techniques to ease correlation and comparison operations. Visual Analytics is often advocated as a doable solution thanks to its ability to enable users to directly obtain insights that support the understanding of the data. However, the grand challenge is to offer to visual analytics software an integrated view on top of multi-source, geo-located, time-varying data. The abstractions described in the FraPPE ontology address this challenge by exploiting classical image processing concepts (i.e. Pixel and Frame), a consolidated geographical data model (i.e. GeoSparql) and a time/event vocabulary (i.e. Time and Event ontologies ). FraPPE was originally developed to represent telecommunication and social media data in an unified way and it is evaluated modeling the dataset made available by ACM DEBS 2015 Grand Challenge.
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References
Alexander, K., Cyganiak, R., Hausenblas, M., Zhao, J.: Describing linked datasets. In: LDOW 2009 (2009). http://ceur-ws.org/Vol-538/ldow2009_paper20.pdf
Balduini, M., Della Valle, E., Azzi, M., Larcher, R., Antonelli, F., Ciuccarelli, P.: Citysensing: visual story telling of city-scale events by fusing social media streams and call data records captured at places and events. IEEE MultiMedia 22(3) (to appear, 2015)
Battle, R., Kolas, D.: Geosparql: enabling a geospatial semantic web. Semantic Web Journal 3(4), 355–370 (2011)
Belhajjame, K., Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., Zednik, S., Zhao, J.: PROV-O: The PROV Ontology. Tech. rep., W3C (2012)
Coscia, M., Rinzivillo, S., Giannotti, F., Pedreschi, D.: Optimal spatial resolution for the analysis of human mobility. In: ASONAM 2012, pp. 248–252 (2012)
Fernández-López, M., Gómez-Pérez, A., Juristo, N.: Methontology: from ontological art towards ontological engineering (1997)
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum.-Comput. Stud. 43(5–6), 907–928 (1995)
Hobbs, J.R., Pan, F.: Time Ontology in OWL, September 2006
Raimond, Y., Abdallah, S.: The event ontology (2007). http://motools.sf.net/event
Rijgersberg, H., van Assem, M., Top, J.L.: Ontology of units of measure and related concepts. Semantic Web 4(1), 3–13 (2013)
Singh, V.K., Gao, M., Jain, R.: Social pixels: genesis and evaluation. In: ICM 2010, pp. 481–490 (2010)
Thomas, J.J., Cook, K.A.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. National Visualization and Analytics Ctr. (2005)
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Balduini, M., Valle, E.D. (2015). FraPPE: A Vocabulary to Represent Heterogeneous Spatio-Temporal Data to Support Visual Analytics. In: Arenas, M., et al. The Semantic Web - ISWC 2015. ISWC 2015. Lecture Notes in Computer Science(), vol 9367. Springer, Cham. https://doi.org/10.1007/978-3-319-25010-6_21
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DOI: https://doi.org/10.1007/978-3-319-25010-6_21
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