Providing easy to use methods for visual analysis of Linked Data is often hindered by the complexity of semantic technologies. On the other hand, semantic information inherent to Linked Data provides opportunities to support the user in interactively analysing the data. This paper provides a demonstration of an interactive, Web-based visualisation tool, the \Vis Wizard", which makes use of semantics to simplify the process of setting up visualisations, transforming the data and, most importantly, interactively analysing multiple datasets using brushing and linking methods.
1. Selecting and con guring the visualisations 2. Aggregating datasets 3. Brushing and linking over multiple datasets
This paper illustrates the use of semantic technologies in a visual analytics tool that enables novice users to perform complex operations and analyses on Linked Data. The demonstration focuses mainly on step 3, with a screencast of the demonstration also being available6.
Related work: A wide range of tools o ers functionalities for visualising and
interacting with data, but only a few rely on semantic information to support the
analytical process. Tableau [
The underlying thought is to make the user capable of visually analysing data without knowing about the concept of Linked Data or RDF Data Cubes. However, the Vis Wizard utilises the available semantic information to support users in interacting with the data and performing analytical tasks. Scenario: Figure 1 compares two datasets taken from the EU Open Data Endpoint7 in the Vis Wizard. The rst one, shown in parallel coordinates, represents the 3G coverage in Europe, as percentage value, per country for each year. The second dataset, shown in the geo-chart, contains active SIM cards per 100 people (encoded by colour-grading) for countries in Europe. In the following we use the Vis Wizard to gain insights into the data and ascertain the datasets correlate.
charts only those are made available which can actively and meaningfully be
used with the provided data. For example, the geo-chart is only available if the
data contains a geographic dimension. After the chart was selected, the user
can adjust the mapping of data onto the visual properties (e.g. axes, colours,
item sizes etc.) of the chart, whereby only suitable mappings are o ered. Chart
selection and the data mapping is computed by an algorithm [
Aggregation: We provide a dialogue for aggregating the data and creating a new Data Cube. In the scenario shown in Fig 1 the second dataset was averaged over the years and visualised over the countries. Using semantics we di erentiate between dimensions and measures and enable validation of the user choices.
For suggesting charts and supporting aggregation we are utilising RDF datatype, occurrence and persistence.
Brushing and Linking: The idea behind brushing and linking is to combine
di erent visualisations to overcome the shortcomings of single techniques [
Applied to our scenario the following interactive analysis is performed (see Fig. 1): The user applies a brush on the rst dataset by selecting a speci c value range in the \3G coverage" dimension using the parallel coordinates chart. Countries outside of the selected range are greyed out in the geo-chart, which shows the second dataset (SIM card penetration). Obviously, a high 3G coverage correlates with high SIM card penetration (red), with one exception - France.
It should be noted that the functionality of linking data over di erent datasets, or even di erent endpoints, depends on the quality of the semantic information: the URIs of the cube-dimensions in di erent datasets need to be consistent. If datasets use di erent, domain-speci c URI namespaces, linking the data will not be possible. 3
We conducted a formative evaluation to explore if our goals regarding the
usability of the Vis Wizard could be achieved and to ascertain that users were able
8 http://code.know-center.tugraz.at/static/ontology/visual-analytics.owl
to analyse complex datasets. Eight test users participated which executed six
tasks, where one task was exclusively about linking and brushing. Test users had
a good knowledge of computers, but were not familiar with semantic data. We
conducted a quantitative subjective workload test, using the simpli ed NASA
R-TLX, and a qualitative thinking aloud test. More details on the evaluation,
including methodology, test users and results are available under [
The functionality supporting the choice and con guration of the visualisation was much appreciated, but users pointed out that immediately suggesting the most suitable visualisation would have been even more helpful. The task regarding brushing in the scatterplot had a very high subjective performance of accomplishing (the median was 91.25 on a scale from 0 to 100, 100 being the highest value achievable). The conclusion of our evaluation is that, while several usability issues still need to be xed, the overall advantage is clearly observable. 4
Within this research we have observed a high potential in using semantic information for improving interaction in visual analytics. It has been shown that the user supporting techniques were helpful in gaining insights from the data, without spending much time in selecting and con guring visualisations or analysing how to link the datasets manually.
As for our purpose the correctness of the semantic annotations of the data is essential, the stability of our approach could be improved by implementing the use of URI aliases. We will also explore the possibilities to rank the visualisations in order to, given a particular dataset, automatically show the most suitable one. Acknowledgements. This work is funded by the EC FP7 projects CODE (grant 296150) and EEXCESS (grant 600601). The Know-Center GmbH is funded by Austrian Federal Government within the Austrian COMET Program, managed by the Austrian Research Promotion Agency (FFG).