210 research outputs found
Visual Analytics for Understanding Draco's Knowledge Base
Draco has been developed as an automated visualization recommendation system
formalizing design knowledge as logical constraints in ASP (Answer-Set
Programming). With an increasing set of constraints and incorporated design
knowledge, even visualization experts lose overview in Draco and struggle to
retrace the automated recommendation decisions made by the system. Our paper
proposes an Visual Analytics (VA) approach to visualize and analyze Draco's
constraints. Our VA approach is supposed to enable visualization experts to
accomplish identified tasks regarding the knowledge base and support them in
better understanding Draco. We extend the existing data extraction strategy of
Draco with a data processing architecture capable of extracting features of
interest from the knowledge base. A revised version of the ASP grammar provides
the basis for this data processing strategy. The resulting incorporated and
shared features of the constraints are then visualized using a hypergraph
structure inside the radial-arranged constraints of the elaborated
visualization. The hierarchical categories of the constraints are indicated by
arcs surrounding the constraints. Our approach is supposed to enable
visualization experts to interactively explore the design rules' violations
based on highlighting respective constraints or recommendations. A qualitative
and quantitative evaluation of the prototype confirms the prototype's
effectiveness and value in acquiring insights into Draco's recommendation
process and design constraints.Comment: To be presented at VIS 202
Event Data Definition in LHCb
We present the approach used for defining the event object model for the LHCb
experiment. This approach is based on a high level modelling language, which is
independent of the programming language used in the current implementation of
the event data processing software. The different possibilities of object
modelling languages are evaluated, and the advantages of a dedicated model
based on XML over other possible candidates are shown. After a description of
the language itself, we explain the benefits obtained by applying this approach
in the description of the event model of an experiment such as LHCb. Examples
of these benefits are uniform and coherent mapping of the object model to the
implementation language across the experiment software development teams, easy
maintenance of the event model, conformance to experiment coding rules, etc.
The description of the object model is parsed by means of a so called
front-end which allows to feed several back-ends. We give an introduction to
the model itself and to the currently implemented back-ends which produce
information like programming language specific implementations of event objects
or meta information about these objects. Meta information can be used for
introspection of objects at run-time which is essential for functionalities
like object persistency or interactive analysis. This object introspection
package for C++ has been adopted by the LCG project as the starting point for
the LCG object dictionary that is going to be developed in common for the LHC
experiments.
The current status of the event object modelling and its usage in LHCb are
presented and the prospects of further developments are discussed.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 7 pages, LaTeX, 2 eps figures. PSN
MOJT00
A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual Analytics
Guidance can support users during the exploration and analysis of complex
data. Previous research focused on characterizing the theoretical aspects of
guidance in visual analytics and implementing guidance in different scenarios.
However, the evaluation of guidance-enhanced visual analytics solutions remains
an open research question. We tackle this question by introducing and
validating a practical evaluation methodology for guidance in visual analytics.
We identify eight quality criteria to be fulfilled and collect expert feedback
on their validity. To facilitate actual evaluation studies, we derive two sets
of heuristics. The first set targets heuristic evaluations conducted by expert
evaluators. The second set facilitates end-user studies where participants
actually use a guidance-enhanced system. By following such a dual approach, the
different quality criteria of guidance can be examined from two different
perspectives, enhancing the overall value of evaluation studies. To test the
practical utility of our methodology, we employ it in two studies to gain
insight into the quality of two guidance-enhanced visual analytics solutions,
one being a work-in-progress research prototype, and the other being a publicly
available visualization recommender system. Based on these two evaluations, we
derive good practices for conducting evaluations of guidance in visual
analytics and identify pitfalls to be avoided during such studies.Comment: Accepted to IEEE VIS 202
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
Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge
Investment planning requires knowledge of the financial landscape on a large
scale, both in terms of geo-spatial and industry sector distribution. There is
plenty of data available, but it is scattered across heterogeneous sources
(newspapers, open data, etc.), which makes it difficult for financial analysts
to understand the big picture. In this paper, we present Sabrina, a financial
data analysis and visualization approach that incorporates a pipeline for the
generation of firm-to-firm financial transaction networks. The pipeline is
capable of fusing the ground truth on individual firms in a region with
(incremental) domain knowledge on general macroscopic aspects of the economy.
Sabrina unites these heterogeneous data sources within a uniform visual
interface that enables the visual analysis process. In a user study with three
domain experts, we illustrate the usefulness of Sabrina, which eases their
analysis process
Visualizing Uncertainty in Sets
Set visualization facilitates the exploration and analysis of set-type data. However, how sets should be visualized when the data are uncertain is still an open research challenge. To address the problem of depicting uncertainty in set visualization, we ask 1) which aspects of set type data can be affected by uncertainty and 2) which characteristics of uncertainty influence the visualization design. We answer these research questions by first describing a conceptual framework that brings together 1) the information that is primarily relevant in sets (i.e., set membership, set attributes, and element attributes) and 2) different plausible categories of (un)certainty (i.e., certainty, undefined uncertainty as a binary fact, and defined uncertainty as quantifiable measure). Following the structure of our framework, we systematically discuss basic visualization examples of integrating uncertainty in set visualizations. We draw on existing knowledge about general uncertainty visualization and previous evidence of its effectiveness
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