Hello everyone, I'm Noemi Mauro
from the University of Torino and today I'm going to present
a faceted exploration model of Cultural Heritage.
The richness of Cultural Heritage
exposes tourists to an information overlad which makes it
difficult to practically visit within a tour.
In general, we can see
maps as a representation of shared data for user
collaboration, for example when they have to organize a tour.
We can also consider maps with a long-lasting
nature, being useful before, during and after
the experience. So, for example, they can
contain artistic information, geographic and logistic one.
However, people travelling together might be
interested in different subsets of the same items based
on their characteristics. So we have to offer
a tool to the users to filter the
data based on their interests.
This work is framed in the OnToMap project.
The goal of the OnToMap project is
to support cartographic
information sharing and visualization in local communities.
OnToMap allows
the consultation of spatial data,
the integration of public Open Data, the creation of
interactive maps and the crowdsourcing of content.
OnToMap has an ontological layer
that is used to integrate heterogeneous data
and manage them as Linked Data
for example, it contains the mappings with the
OpenStreetMap tags,
it contains the definition of data categories and
facets and it's also useful to
specify graphical details for map visualization
for example, it contains categories
colors and icons.
Here, we can see the OnToMap web application.
On the left side, we can see that there
are the graphical widgets for the searched categories
Then, we have markers on the map that are
represented with icons that
graphically represent data categories
and then in the
right side of the web interface we have the details
of an item.
The problem that we are going to face is that
maps can be really cluttered containing a
lot of information, as you can see in the picture.
So, our goal is to offer a tool that
allows the creation of custom maps that can
be adapted to reflect temporary information
goals while persistently storing the shared data in order
to preserve the overall collaboration context.
So, we want to support interactive coarse-grained
and fine-grained focusing of
geographic maps via interactive widgets.
So, we have two proposals: the first one concerns
an interactive representation of the
search context for
multi-category projection based on widgets that
support: data visualization, faceted exploration,
category-based information hiding and
transparency of results.
And then the second proposal concerns
a function to define the exploration cost of facets
based on the suggestion of efficient facets for
data exploration in sparse and noisy datasets.
Here, I'm going to present the two widgets
that we investigated, the first one concerns
the checkboxes that are the typical tool that we can find
in e-commerce and booking websites.
So, on the left side we have the
"Places of Worship" category with
its attributes, so for example "Religion"
and then with the checkboxes we can filter the values.
In this case, we selected "Christian" and "Muslim".
Then, when we select a
specific Point of Interest that has been filtered
we will have highlighted in the details
the values that we selected.
The second widget is represented by
the sunburst. The sunburst is designed
with these two circles.
The one in the middle represents the attributes while
on the second level
we have the values corresponding to the attributes.
So, in this way,
we are able to see all the attributes together
and we are able to have an overview of all
the characteristics of the selected category.
After that we defined an exploration cost function
in order to define which facets should
be displayed on the user interface
and also to rank them according to their
ability to filter the information items.
So, we first did a pre-filtering phase in order to exclude
the facets that appear very rarely in the results
and then we define the exploration cost
function that is computed by
taking into account the entropy and dividing it
with the mean cardinality of the facet.
We couldn't use functions that were
present in the literature because all these functions
were giving priorities to balanced facets.
However, when we deal with Open Data we often have
unbalanced facets but that they are still
able to filter the information space. For example,
if we take into account the "Cuisine" facet
with our function, it will be
highlighted with a yellow color, so it will be ranked in the
middle place of the list
while if we take into account one of the
functions present in the literature
the facet "Cuisine" will be highlighted in red
and so probably it will not be displayed.
For the validation of the visualization model
we asked to the users to solve two map learning tasks.
The tasks were question answering
using checkboxes and sunburst and
for each task we asked to the participants to
answer to two questions: the first question
was like: how many category names
for example "churches", have the characteristic_x
for example "that are accessible with wheelchairs"
are visualized within the orange line in the map.
And the second question was like: find
category names for example "restaurants" having
characteristic_x, for example "chinese"
within the orange line in the map, and list them.
For what concerns the experimental setting, it was
a within subject design. We investigated
the evaluation of the
two types of information exploration widgets.
We counterbalanced the tasks in order to minimize
the effect of practice and fatigue and
we asked to the participants to sign a privacy consensus
according to the GDPR.
We measured objective and subjective measures.
For what concerns the objective measures we measured
the execution time and the percentage of correct answers
and for what concerns the subjective measure we asked to the
user to fill a questionnaire at the end of each
task and a post-test questionnaire at the end of the user study
to measure the user experience. The duration time
of the test was about 30 minutes
and we investigated the familiarity,
the helpfulness, the effort saving, the easy of use and the novelty
of the widget. We also asked to the user to answer to
some free-text questions in which we asked
if they had difficulties, which widget they
will use or avoid in the future and
if it was useful for them to combine
transparency sliders with other widgets.
We interviewed 62 participants.
The majority of them had an high education level,
they often use the online maps
and they mostly have a
scientific and technical background but some of them
also had other backgrounds and
they are also often using e-commerce platforms.
The results show that the participants correctly
answered to all the questions and that the sunburst
had the lowest execution time. For what
concerns the user experience
the best widget for what concerns the familiarity
was the checkboxes. Both of them were
helpful and effort saving.
Users said that checkboxes have a better easy of use while
the more novel widget was the sunburst.
In the future, they said that they will use again the sunburst.
So, in general we can say that the sunburst is a good
model to compactly visualize all
the facets and values of a data category and it
also minimizes the vertical expansion of the sidebar
with respect to the checkboxes. We notice that
there is a frequent usage pattern.
So, users often combine coarse-grained infomation
hiding with the transparency sliders
with fine-grained faceted selection using
checkboxes or sunburst.
So, they first reduce the complexity of the map by hiding
all the items belonging to the categories that
they are not of interest and then
they focus on the details of the categories
of interest using checkboxes or sunburst.
As future work, we plan to adapt the facets suggestion
to the user's preferences in order to personalize
the navigation of the information space.
We plan to add support to collaborative work
"in synch". We will investigate this aspect
in further experiments. And we also plan to
adapt and test our model in mobile settings
and possibly using small displays such as those
of mobile phones.
Thank you for your attention!