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2019 | OriginalPaper | Chapter

SILKNOWViz: Spatio-Temporal Data Ontology Viewer

Authors : Javier Sevilla, Cristina Portalés, Jesús Gimeno, Jorge Sebastián

Published in: Computational Science – ICCS 2019

Publisher: Springer International Publishing

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Abstract

Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphic objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also emphasizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data selection methods.

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Literature
1.
go back to reference Kehrer, J., Hauser, H.: Visualization and visual analysis of multifaceted scientific data: a survey. IEEE Trans. Visual Comput. Graphics 19, 495–513 (2013)CrossRef Kehrer, J., Hauser, H.: Visualization and visual analysis of multifaceted scientific data: a survey. IEEE Trans. Visual Comput. Graphics 19, 495–513 (2013)CrossRef
2.
go back to reference Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Visual Comput. 30, 1373–1393 (2014)CrossRef Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Visual Comput. 30, 1373–1393 (2014)CrossRef
3.
go back to reference Lavalle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52, 21–32 (2011) Lavalle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52, 21–32 (2011)
5.
go back to reference Bach, B., Dragicevic, P., Archambault, D., Hurter, C., Carpendale, S.: A review of temporal data visualizations based on space-time cube operations. In: Eurographics Conference on Visualization (EuroVis 2014), pp. 23–41 (2014) Bach, B., Dragicevic, P., Archambault, D., Hurter, C., Carpendale, S.: A review of temporal data visualizations based on space-time cube operations. In: Eurographics Conference on Visualization (EuroVis 2014), pp. 23–41 (2014)
6.
go back to reference Gruninger, M., et al.: Ontology Summit 2014 Communique: Semantic Web and Big Data Meet Applied Ontology (2014) Gruninger, M., et al.: Ontology Summit 2014 Communique: Semantic Web and Big Data Meet Applied Ontology (2014)
7.
go back to reference Bennett, M., Baclawski, K.: The role of ontologies in linked data, big data and semantic web applications. App. Ontol. 12, 189–194 (2017)CrossRef Bennett, M., Baclawski, K.: The role of ontologies in linked data, big data and semantic web applications. App. Ontol. 12, 189–194 (2017)CrossRef
8.
go back to reference Baclawski, K., et al.: Ontology Summit 2018 Communiqué: Contexts in Context (2018)CrossRef Baclawski, K., et al.: Ontology Summit 2018 Communiqué: Contexts in Context (2018)CrossRef
10.
go back to reference Dou, D., Wang, H., Liu, H.: Semantic data mining: a survey of ontology-based approaches. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), pp. 244–251 (2015) Dou, D., Wang, H., Liu, H.: Semantic data mining: a survey of ontology-based approaches. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), pp. 244–251 (2015)
11.
go back to reference Neches, R., et al.: Enabling technology for knowledge sharing. AI Mag. 12, 36–56 (1991) Neches, R., et al.: Enabling technology for knowledge sharing. AI Mag. 12, 36–56 (1991)
12.
go back to reference Guo, D., Du, Y.: A visualization platform for spatio-temporal data: a data intensive computation framework. In: 2015 23rd International Conference on Geoinformatics, pp. 1–6 (2015) Guo, D., Du, Y.: A visualization platform for spatio-temporal data: a data intensive computation framework. In: 2015 23rd International Conference on Geoinformatics, pp. 1–6 (2015)
13.
go back to reference Dudáš, M., Lohmann, S., Svátek, V., Pavlov, D.: Ontology visualization methods and tools: a survey of the state of the art. Knowl. Eng. Rev. 33, e10 (2018)CrossRef Dudáš, M., Lohmann, S., Svátek, V., Pavlov, D.: Ontology visualization methods and tools: a survey of the state of the art. Knowl. Eng. Rev. 33, e10 (2018)CrossRef
14.
go back to reference Nazemi, K., Burkhardt, D., Ginters, E., Kohlhammer, J.: Semantics visualization – definition, approaches and challenges. Procedia Comput. Sci. 75, 75–83 (2015)CrossRef Nazemi, K., Burkhardt, D., Ginters, E., Kohlhammer, J.: Semantics visualization – definition, approaches and challenges. Procedia Comput. Sci. 75, 75–83 (2015)CrossRef
15.
go back to reference Falconer, S.M., Bull, R.I., Grammel, L., Storey, M.: Creating visualizations through ontology mapping. In: 2009 International Conference on Complex, Intelligent and Software Intensive Systems, pp. 688–693 (2009) Falconer, S.M., Bull, R.I., Grammel, L., Storey, M.: Creating visualizations through ontology mapping. In: 2009 International Conference on Complex, Intelligent and Software Intensive Systems, pp. 688–693 (2009)
17.
go back to reference Polowinski, J.: Towards RVL: A declarative language for visualizing RDFS/OWL data. In: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, pp. 38:1–38:11. ACM, New York (2013) Polowinski, J.: Towards RVL: A declarative language for visualizing RDFS/OWL data. In: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, pp. 38:1–38:11. ACM, New York (2013)
18.
go back to reference Peuquet, D.J.: It’s about time: a conceptual framework for the representation of temporal dynamics in geographic information systems. Ann. Assoc. Am. Geogr. 84, 441–461 (1994)CrossRef Peuquet, D.J.: It’s about time: a conceptual framework for the representation of temporal dynamics in geographic information systems. Ann. Assoc. Am. Geogr. 84, 441–461 (1994)CrossRef
19.
go back to reference Andrienko, N., Andrienko, G., Gatalsky, P.: Exploratory spatio-temporal visualization: an analytical review. J. Visual Lang. Comput. 14, 503–541 (2003)CrossRef Andrienko, N., Andrienko, G., Gatalsky, P.: Exploratory spatio-temporal visualization: an analytical review. J. Visual Lang. Comput. 14, 503–541 (2003)CrossRef
20.
go back to reference Ku, W.-Y., et al.: An online atlas for exploring spatio-temporal patterns of cancer mortality (1972–2011) and incidence (1995–2008) in Taiwan. Medicine 95, e3496–e3496 (2016)CrossRef Ku, W.-Y., et al.: An online atlas for exploring spatio-temporal patterns of cancer mortality (1972–2011) and incidence (1995–2008) in Taiwan. Medicine 95, e3496–e3496 (2016)CrossRef
21.
go back to reference Hengl, T., Roudier, P., Beaudette, D., Pebesma, E.: plotKML: scientific visualization of spatio-temporal data. J. Stat. Softw. 63, 1–25 (2015)CrossRef Hengl, T., Roudier, P., Beaudette, D., Pebesma, E.: plotKML: scientific visualization of spatio-temporal data. J. Stat. Softw. 63, 1–25 (2015)CrossRef
22.
go back to reference Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks (2005) Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks (2005)
23.
go back to reference Jänicke, S., Heine, C., Scheuermann, G.: GeoTemCo: comparative visualization of geospatial-temporal data with clutter removal based on dynamic delaunay triangulations. In: Csurka, G., Kraus, M., Laramee, Robert S., Richard, P., Braz, J. (eds.) VISIGRAPP 2012. CCIS, vol. 359, pp. 160–175. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38241-3_11CrossRef Jänicke, S., Heine, C., Scheuermann, G.: GeoTemCo: comparative visualization of geospatial-temporal data with clutter removal based on dynamic delaunay triangulations. In: Csurka, G., Kraus, M., Laramee, Robert S., Richard, P., Braz, J. (eds.) VISIGRAPP 2012. CCIS, vol. 359, pp. 160–175. Springer, Heidelberg (2013). https://​doi.​org/​10.​1007/​978-3-642-38241-3_​11CrossRef
24.
go back to reference Kraak, M.J.: Timelines, temporal resolution, temporal zoom and time geography (2005) Kraak, M.J.: Timelines, temporal resolution, temporal zoom and time geography (2005)
25.
go back to reference Lee, C., Devillers, R., Hoeber, O.: Navigating spatio-temporal data with temporal zoom and pan in a multi-touch environment. Int. J. Geogr. Inf. Sci. 28, 1128–1148 (2014)CrossRef Lee, C., Devillers, R., Hoeber, O.: Navigating spatio-temporal data with temporal zoom and pan in a multi-touch environment. Int. J. Geogr. Inf. Sci. 28, 1128–1148 (2014)CrossRef
26.
go back to reference Wang, C., Ma, X., Chen, J.: Ontology-driven data integration and visualization for exploring regional geologic time and paleontological information. Comput. Geosci. 115, 12–19 (2018)CrossRef Wang, C., Ma, X., Chen, J.: Ontology-driven data integration and visualization for exploring regional geologic time and paleontological information. Comput. Geosci. 115, 12–19 (2018)CrossRef
27.
go back to reference Hewagamage, K., Hirakawa, M., Ichikawa, T.: Interactive Visualization of Spatiotemporal Patterns Using Spirals on a Geographical Map (1999) Hewagamage, K., Hirakawa, M., Ichikawa, T.: Interactive Visualization of Spatiotemporal Patterns Using Spirals on a Geographical Map (1999)
28.
go back to reference Guo, D.: Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans. Visual Comput. Graphics 15, 1041–1048 (2009)CrossRef Guo, D.: Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans. Visual Comput. Graphics 15, 1041–1048 (2009)CrossRef
30.
go back to reference Zerdoumi, S., et al.: Image pattern recognition in big data: taxonomy and open challenges: survey. Multimedia Tools Appl. 77, 10091–10121 (2018)CrossRef Zerdoumi, S., et al.: Image pattern recognition in big data: taxonomy and open challenges: survey. Multimedia Tools Appl. 77, 10091–10121 (2018)CrossRef
31.
go back to reference Zhao, B., et al.: Ontobee: a linked ontology data server to support ontology term dereferencing, linkage, query and integration. Nucleic Acids Res. 45, D347–D352 (2016) Zhao, B., et al.: Ontobee: a linked ontology data server to support ontology term dereferencing, linkage, query and integration. Nucleic Acids Res. 45, D347–D352 (2016)
32.
go back to reference Verhodubs, O.: Realization of Ontology Web Search Engine (2017) Verhodubs, O.: Realization of Ontology Web Search Engine (2017)
34.
go back to reference CIDOC Documentation Standards Group: CIDOC Conceptual Reference Model (CRM) – ISO 21127:2006 (2006) CIDOC Documentation Standards Group: CIDOC Conceptual Reference Model (CRM) – ISO 21127:2006 (2006)
Metadata
Title
SILKNOWViz: Spatio-Temporal Data Ontology Viewer
Authors
Javier Sevilla
Cristina Portalés
Jesús Gimeno
Jorge Sebastián
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-22750-0_8

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