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.
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Notes
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Open Data Barometer, 2013 Global Report.
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“YourDataStories” is distributed through github, under the GPL v.3 license: https://github.com/YourDataStories.
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The application has been tested with OpenLink Virtuoso.
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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.
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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
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