Abstract
While there are several existing infrastructures for data analytics, mainly oriented towards the analysis of tabular data, the integration of analytics with linked data is still a challenging task. There are several reasons for this lack of interoperability between linked data and analytics, including representation issues and inability to exploit the full potential of linked data. In this paper we present “YourDataStories”, an infrastructure that tries to minimise the gap between linked data (where RDF is dominant) and Web-based analytics platforms (where JSON is dominant), through the use of technologies like JSON-LD, Hydra, and a set of reusable visualisation components, which do not depend on a specific semantic model and can adapt to almost any semantic model. The presented infrastructure has been used to create a set of visualisation applications, ranging from Web visual applications (such as facet search, dashboards, and “wizard-like” interfaces allowing various kind of stakeholders to find, understand and consume the data), to mobile and social media applications that inform citizens and gather their feedback. Having as a central component the model analyser, which retrieves and analyses a semantic model (based on OWL and SKOS) from a triple store, the resulting infrastructure and applications allow users to “drill-down” on the available data and extract subsets of interest, which can be analysed through pre-configured and user-driven custom visualisations and dashboards.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Open Data Barometer, 2013 Global Report.
- 3.
- 4.
- 5.
“YourDataStories” is distributed through github, under the GPL v.3 license: https://github.com/YourDataStories.
- 6.
The application has been tested with OpenLink Virtuoso.
- 7.
- 8.
- 9.
- 10.
References
Brunetti, J.M., Auer, S., García, R., Klímek, J., Nečaský, M.: Formal linked data visualization model. In: Proceedings of the International Conference on Information Integration and Web-Based Applications& Services, IIWAS 2013, pp. 309:309–309:318. ACM, New York (2013)
W3C Recommendation: SKOS simple knowledge organization system reference (2009). https://www.w3.org/TR/skos-reference/
Baron Neto, C., Müller, K., Brümmer, M., Kontokostas, D., Hellmann, S.: LODVader: an interface to LOD visualization, analytics and discovery in real-time. In: Proceedings of the 25th International Conference Companion on World Wide Web, WWW 2016 Companion, pp. 163–166. International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland (2016)
Voigt, M., Tietz, V., Piccolotto, N., Meißner, K.: Attract me!: How could end-users identify interesting resources? In: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, WIMS 2013, pp. 36:1–36:12. ACM, New York (2013)
Voigt, M., Pietschmann, S., Meißner, K.: A semantics-based, end-user-centered information visualization process for semantic web data. In: Hussein, T., Paulheim, H., Lukosch, S., Ziegler, J., Calvary, G. (eds.) Semantic Models for Adaptive Interactive Systems, pp. 83–107. Springer, London (2013). https://doi.org/10.1007/978-1-4471-5301-6_5
Bikakis, N., Skourla, M., Papastefanatos, G.: rdf:SynopsViz – a framework for hierarchical linked data visual exploration and analysis. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8798, pp. 292–297. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11955-7_37
Mutlu, B., Hoefler, P., Tschinkel, G., Veas, E., Sabol, V., Stegmaier, F., Granitzer, M.: Suggesting visualisations for published data. In: 2014 International Conference on Information Visualization Theory and Applications (IVAPP), pp. 267–275, January 2014
Hoefler, P., Granitzer, M., Sabol, V., Lindstaedt, S.: Linked data query wizard: a tabular interface for the semantic web. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 173–177. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41242-4_19
Martin, M., Abicht, K., Stadler, C., Ngonga Ngomo, A.C., Soru, T., Auer, S.: CubeViz: exploration and visualization of statistical linked data. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 219–222. ACM, New York (2015)
The RDF Data Cube Vocabulary: SKOS simple knowledge organization system reference (2014). https://www.w3.org/TR/vocab-data-cube/
Thellmann, K., Galkin, M., Orlandi, F., Auer, S.: LinkDaViz – automatic binding of linked data to visualizations. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 147–162. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_9
W3C Recommendation: OWL 2 web ontology language document overview, 2nd edn. (2012). https://www.w3.org/TR/2012/REC-owl2-overview-20121211/
JSON-LD: A JSON-based serialization for linked data (2016). http://www.json-ld.org/
Hydra: Hydra W3C community group (2016). http://www.hydra-cg.com/
Acknowledgments
This paper is supported by the project “Your Data Stories – YDS”, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 645886.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Petasis, G., Triantafillou, A., Karstens, E. (2018). YourDataStories: Transparency and Corruption Fighting Through Data Interlinking and Visual Exploration. In: Diplaris, S., Satsiou, A., Følstad, A., Vafopoulos, M., Vilarinho, T. (eds) Internet Science. INSCI 2017. Lecture Notes in Computer Science(), vol 10750. Springer, Cham. https://doi.org/10.1007/978-3-319-77547-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-77547-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77546-3
Online ISBN: 978-3-319-77547-0
eBook Packages: Computer ScienceComputer Science (R0)