62 research outputs found
Telling Stories about Dynamic Networks with Graph Comics
International audienceIn this paper, we explore graph comics as a medium to communicate changes in dynamic networks. While previous research has focused on visualizing dynamic networks for data exploration, we want to see if we can take advantage of the visual expressiveness and familiarity of comics to present and explain temporal changes in networks to an audience. To understand the potential of comics as a storytelling medium, we first created a variety of comics during a 3 month structured design process, involving domain experts from public education and neuroscience. This process led to the definition of 8 design factors for creating graph comics and propose design solutions for each. Results from a qualitative study suggest that a general audience is quickly able understand complex temporal changes through graph comics, provided with minimal textual annotations and no training
A Review and Characterization of Progressive Visual Analytics
Progressive Visual Analytics (PVA) has gained increasing attention over the past years.
It brings the user into the loop during otherwise long-running and non-transparent computations
by producing intermediate partial results. These partial results can be shown to the user
for early and continuous interaction with the emerging end result even while it is still being
computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth
various interpretations and instantiations that have created a research domain of competing terms,
various definitions, as well as long lists of practical requirements and design guidelines spread across
different scientific communities. This makes it more and more difficult to get a succinct understanding
of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and
discussion of PVA presented in this paper address these issues and provide (1) a literature collection
on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical
recommendations for implementing and using PVA-based visual analytics solutions
Visualising Business Data: A Survey
A rapidly increasing number of businesses rely on visualisation solutions for their data management challenges. This demand stems from an industry-wide shift towards data-driven approaches to decision making and problem-solving. However, there is an overwhelming mass of heterogeneous data collected as a result. The analysis of these data become a critical and challenging part of the business process. Employing visual analysis increases data comprehension thus enabling a wider range of users to interpret the underlying behaviour, as opposed to skilled but expensive data analysts. Widening the reach to an audience with a broader range of backgrounds creates new opportunities for decision making, problem-solving, trend identification, and creative thinking. In this survey, we identify trends in business visualisation and visual analytic literature where visualisation is used to address data challenges and identify areas in which industries use visual design to develop their understanding of the business environment. Our novel classification of literature includes the topics of businesses intelligence, business ecosystem, customer-centric. This survey provides a valuable overview and insight into the business visualisation literature with a novel classification that highlights both mature and less developed research directions
Perspectives of Visualization Onboarding and Guidance in VA
A typical problem in Visual Analytics is that users are highly trained
experts in their application domains, but have mostly no experience in using VA
systems. Thus, users often have difficulties interpreting and working with
visual representations. To overcome these problems, user assistance can be
incorporated into VA systems to guide experts through the analysis while
closing their knowledge gaps. Different types of user assistance can be applied
to extend the power of VA, enhance the user's experience, and broaden the
audience for VA. Although different approaches to visualization onboarding and
guidance in VA already exist, there is a lack of research on how to design and
integrate them in effective and efficient ways. Therefore, we aim at putting
together the pieces of the mosaic to form a coherent whole. Based on the
Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of
user assistance for VA by integrating the process of visualization onboarding
and guidance as the two main approaches in this direction. As a result, we
clarify and discuss the commonalities and differences between visualization
onboarding and guidance, and discuss how they benefit from the integration of
knowledge extraction and exploration. Finally, we discuss our descriptive model
by applying it to VA tools integrating visualization onboarding and guidance,
and showing how they should be utilized in different phases of the analysis in
order to be effective and accepted by the user.Comment: Elsevier Visual Informatics (revised version under review
Multi-field Visualisation via Trait-induced Merge Trees
In this work, we propose trait-based merge trees a generalization of merge
trees to feature level sets, targeting the analysis of tensor field or general
multi-variate data. For this, we employ the notion of traits defined in
attribute space as introduced in the feature level sets framework. The
resulting distance field in attribute space induces a scalar field in the
spatial domain that serves as input for topological data analysis. The leaves
in the merge tree represent those areas in the input data that are closest to
the defined trait and thus most closely resemble the defined feature. Hence,
the merge tree yields a hierarchy of features that allows for querying the most
relevant and persistent features. The presented method includes different query
methods for the tree which enable the highlighting of different aspects. We
demonstrate the cross-application capabilities of this approach with three case
studies from different domains
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