39,433 research outputs found
Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data
The collection of large, complex datasets has become common across a wide
variety of domains. Visual analytics tools increasingly play a key role in
exploring and answering complex questions about these large datasets. However,
many visualizations are not designed to concurrently visualize the large number
of dimensions present in complex datasets (e.g. tens of thousands of distinct
codes in an electronic health record system). This fact, combined with the
ability of many visual analytics systems to enable rapid, ad-hoc specification
of groups, or cohorts, of individuals based on a small subset of visualized
dimensions, leads to the possibility of introducing selection bias--when the
user creates a cohort based on a specified set of dimensions, differences
across many other unseen dimensions may also be introduced. These unintended
side effects may result in the cohort no longer being representative of the
larger population intended to be studied, which can negatively affect the
validity of subsequent analyses. We present techniques for selection bias
tracking and visualization that can be incorporated into high-dimensional
exploratory visual analytics systems, with a focus on medical data with
existing data hierarchies. These techniques include: (1) tree-based cohort
provenance and visualization, with a user-specified baseline cohort that all
other cohorts are compared against, and visual encoding of the drift for each
cohort, which indicates where selection bias may have occurred, and (2) a set
of visualizations, including a novel icicle-plot based visualization, to
compare in detail the per-dimension differences between the baseline and a
user-specified focus cohort. These techniques are integrated into a medical
temporal event sequence visual analytics tool. We present example use cases and
report findings from domain expert user interviews.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG),
Volume 26 Issue 1, 2020. Also part of proceedings for IEEE VAST 201
Inviwo -- A Visualization System with Usage Abstraction Levels
The complexity of today's visualization applications demands specific
visualization systems tailored for the development of these applications.
Frequently, such systems utilize levels of abstraction to improve the
application development process, for instance by providing a data flow network
editor. Unfortunately, these abstractions result in several issues, which need
to be circumvented through an abstraction-centered system design. Often, a high
level of abstraction hides low level details, which makes it difficult to
directly access the underlying computing platform, which would be important to
achieve an optimal performance. Therefore, we propose a layer structure
developed for modern and sustainable visualization systems allowing developers
to interact with all contained abstraction levels. We refer to this interaction
capabilities as usage abstraction levels, since we target application
developers with various levels of experience. We formulate the requirements for
such a system, derive the desired architecture, and present how the concepts
have been exemplary realized within the Inviwo visualization system.
Furthermore, we address several specific challenges that arise during the
realization of such a layered architecture, such as communication between
different computing platforms, performance centered encapsulation, as well as
layer-independent development by supporting cross layer documentation and
debugging capabilities
What May Visualization Processes Optimize?
In this paper, we present an abstract model of visualization and inference
processes and describe an information-theoretic measure for optimizing such
processes. In order to obtain such an abstraction, we first examined six
classes of workflows in data analysis and visualization, and identified four
levels of typical visualization components, namely disseminative,
observational, analytical and model-developmental visualization. We noticed a
common phenomenon at different levels of visualization, that is, the
transformation of data spaces (referred to as alphabets) usually corresponds to
the reduction of maximal entropy along a workflow. Based on this observation,
we establish an information-theoretic measure of cost-benefit ratio that may be
used as a cost function for optimizing a data visualization process. To
demonstrate the validity of this measure, we examined a number of successful
visualization processes in the literature, and showed that the
information-theoretic measure can mathematically explain the advantages of such
processes over possible alternatives.Comment: 10 page
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