Open Access
Towards a linked-data based visualization wizard
Ghislain Auguste Atemezing,Raphaël Troncy +1 more
- 20 Oct 2014
- pp 1-12
TL;DR: A novel approach that mines the content of datasets and automatically generates visualizations based on the usage of SPARQL queries that will detect the important categories of a dataset and that will specifically consider the properties used by the objects which have been interlinked via owl:sameAs links is proposed.
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Abstract: Datasets published in the LOD cloud are recommended to follow some best practice in order to be 4-5 stars Linked Data compliant. They can often be consumed and accessed by different means such as API access, bulk download or as linked data fragments, but most of the time, a SPARQL endpoint is also provided. While the LOD cloud keeps growing, having a quick glimpse of those datasets is getting harder and there is a need to develop new methods enabling to detect automatically what an arbitrary dataset is about and to recommend visualizations for data samples. We consider that "a visualization is worth a million triples", and in this paper, we propose a novel approach that mines the content of datasets and automatically generates visualizations. Our approach is directly based on the usage of SPARQL queries that will detect the important categories of a dataset and that will specifically consider the properties used by the objects which have been interlinked via owl:sameAs links. We then propose to associate type of visualization for those categories. We have implemented this approach into a so-called Linked Data Vizualization Wizard (LDVizWiz).
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Citations
Semantic Web in data mining and knowledge discovery
Petar Ristoski,Heiko Paulheim +1 more
TL;DR: The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.
324
Characterizing Guidance in Visual Analytics
Davide Ceneda,Theresia Gschwandtner,Thorsten May,Silvia Miksch,Hans-Jörg Schulz,Marc Streit,Christian Tominski +6 more
TL;DR: A general model that facilitates in-depth reasoning about guidance is established by extending van Wijk's model of visualization with the fundamental components of guidance, which is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis.
243
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Linked Data Visualization: Techniques, Tools, and Big Data
Laura Po,Nikos Bikakis,Federico Desimoni,George Papastefanatos +3 more
- 20 Mar 2020
TL;DR: This paper presents a meta-modelling architecture that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging individual pieces of data to create a graph of their contents.
Big Data Visualization Tools
Nikos Bikakis
- 01 Jan 2018
TL;DR: Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation as discussed by the authors, providing users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities.
35
Visualizing large knowledge graphs: a performance analysis
TL;DR: It is shown that distributed implementations of the graph building, metric calculation and layout stages can efficiently manage very large graphs, even without applying partitioning or incremental processing strategies.
33
References
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Sgvizler: A JavaScript Wrapper for Easy Visualization of SPARQL Result Sets
Martin G. Skjæveland
- 27 May 2012
TL;DR: Sgvizler is a small JavaScript wrapper for visualization of SParQL results sets that integrates well with HTML web pages by letting the user specify SPARQL SELECT queries directly into designated HTML elements, which are rendered to contain the specified visualization type on page load or on function call.
Publishing Statistical Data on the Web
Percy E. Rivera Salas,Michael Martin,Fernando Maia Da Mota,Sören Auer,Karin Breitman,Marco A. Casanova +5 more
- 19 Sep 2012
TL;DR: This article overviews how statistical data can be managed on the Web with OLAP2 Data Cube and CSV2 DataCube and presents two complementary approaches on how to extract and publish statistical data.