SILKNOWViz: Spatio-Temporal Data Ontology Viewer
Pages 97 - 109
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.
References
[1]
Kehrer J and Hauser H Visualization and visual analysis of multifaceted scientific data: a survey IEEE Trans. Visual Comput. Graphics 2013 19 495-513
[2]
Liu S, Cui W, Wu Y, and Liu M A survey on information visualization: recent advances and challenges Visual Comput. 2014 30 1373-1393
[3]
Lavalle S, Lesser E, Shockley R, Hopkins MS, and Kruschwitz N Big data, analytics and the path from insights to value MIT Sloan Manag. Rev. 2011 52 21-32
[4]
Zhong C, Wang T, Zeng W, and Müller Arisona S Arisona SM, Aschwanden G, Halatsch J, and Wonka P Spatiotemporal visualisation: a survey and outlook Digital Urban Modeling and Simulation 2012 Heidelberg Springer 299-317
[5]
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]
Gruninger, M., et al.: Ontology Summit 2014 Communique: Semantic Web and Big Data Meet Applied Ontology (2014)
[7]
Bennett M and Baclawski K The role of ontologies in linked data, big data and semantic web applications App. Ontol. 2017 12 189-194
[8]
Baclawski Kenneth, Bennett Mike, Berg-Cross Gary, Casanave Cory, Fritzsche Donna, Luciano Joanne, Schneider Todd, Sharma Ravi, Singer Janet, Sowa John, Sriram Ram D., Westerinen Andrea, and Whitten David Ontology Summit 2018 Communiqué: Contexts in context Applied Ontology 2018 13 3 181-200
[9]
Konys A Kobayashi S-y, Piegat A, Pejaś J, El Fray I, and Kacprzyk J Ontology-based approaches to big data analytics Hard and Soft Computing for Artificial Intelligence, Multimedia and Security 2017 Cham Springer 355-365
[10]
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]
Neches R et al. Enabling technology for knowledge sharing AI Mag. 1991 12 36-56
[12]
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]
Dudáš M, Lohmann S, Svátek V, and Pavlov D Ontology visualization methods and tools: a survey of the state of the art Knowl. Eng. Rev. 2018 33 e10
[14]
Nazemi K, Burkhardt D, Ginters E, and Kohlhammer J Semantics visualization – definition, approaches and challenges Procedia Comput. Sci. 2015 75 75-83
[15]
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)
[16]
Polowinski, J., Voigt, M.: VISO: a shared, formal knowledge base as a foundation for semi-automatic InfoVis systems. In: CHI ’13 Extended Abstracts on Human Factors in Computing Systems (CHI EA 2013). ACM (2013).
[17]
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]
Peuquet DJ It’s about time: a conceptual framework for the representation of temporal dynamics in geographic information systems Ann. Assoc. Am. Geogr. 1994 84 441-461
[19]
Andrienko N, Andrienko G, and Gatalsky P Exploratory spatio-temporal visualization: an analytical review J. Visual Lang. Comput. 2003 14 503-541
[20]
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 2016 95 e3496-e3496
[21]
Hengl T, Roudier P, Beaudette D, and Pebesma E plotKML: scientific visualization of spatio-temporal data J. Stat. Softw. 2015 63 1-25
[22]
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks (2005)
[23]
Jänicke S, Heine C, and Scheuermann G Csurka G, Kraus M, Laramee Robert S, Richard P, and Braz J GeoTemCo: comparative visualization of geospatial-temporal data with clutter removal based on dynamic delaunay triangulations Computer Vision, Imaging and Computer Graphics. Theory and Application 2013 Heidelberg Springer 160-175
[24]
Kraak, M.J.: Timelines, temporal resolution, temporal zoom and time geography (2005)
[25]
Lee C, Devillers R, and Hoeber O Navigating spatio-temporal data with temporal zoom and pan in a multi-touch environment Int. J. Geogr. Inf. Sci. 2014 28 1128-1148
[26]
Wang C, Ma X, and Chen J Ontology-driven data integration and visualization for exploring regional geologic time and paleontological information Comput. Geosci. 2018 115 12-19
[27]
Hewagamage, K., Hirakawa, M., Ichikawa, T.: Interactive Visualization of Spatiotemporal Patterns Using Spirals on a Geographical Map (1999)
[28]
Guo D Flow mapping and multivariate visualization of large spatial interaction data IEEE Trans. Visual Comput. Graphics 2009 15 1041-1048
[29]
Google Maps (2019). https://cloud.google.com/maps-platform/
[30]
Zerdoumi S et al. Image pattern recognition in big data: taxonomy and open challenges: survey Multimedia Tools Appl. 2018 77 10091-10121
[31]
Zhao B et al. Ontobee: a linked ontology data server to support ontology term dereferencing, linkage, query and integration Nucleic Acids Res. 2016 45 D347-D352
[32]
Verhodubs, O.: Realization of Ontology Web Search Engine (2017)
[33]
Dudáš M, Zamazal O, and Svátek V Janowicz K, Schlobach S, Lambrix P, and Hyvönen E Roadmapping and navigating in the ontology visualization landscape Knowledge Engineering and Knowledge Management 2014 Cham Springer 137-152
[34]
CIDOC Documentation Standards Group: CIDOC Conceptual Reference Model (CRM) – ISO 21127:2006 (2006)
[35]
Erlangen CRM/OWL. CIDOC-CRM Implementation (2013). http://erlangen-crm.org/
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Published In
Jun 2019
798 pages
ISBN:978-3-030-22749-4
DOI:10.1007/978-3-030-22750-0
© Springer Nature Switzerland AG 2019.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 12 June 2019
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