68,500 research outputs found
Visualization of state transition graphs
State transition graphs are important in computer science and engineering where they are used to analyze the behavior of computer-based systems. In such a graph nodes represent states a system can be in. Links, or directed edges, represent transitions between states. Research in visualization investigates the application of interactive computer graphics to understand large and complex data sets. Large state transition graphs fall into this category. They often contain tens of thousands of nodes, or more, and tens to hundreds of thousands of edges. Also, they describe system behavior at a low abstraction level. This hinders analysis and insight. This dissertation presents a number of techniques for the interactive visualization of state transition graphs. Much of the work takes advantage of multivariate data associated with nodes and edges. Using an experimental approach, several new methods were developed in close collaboration with a number of users. The following approaches were pursued: • Selection and projection. This technique provides the user with visual support to select a subset of node attributes. Consequently, the state transition graph is projected to 2D and visualized in a second, correlated visualization. • Attribute-based clustering. By specifying subsets of node attributes and clustering based on these, the user generates simplified abstractions of a state transition graph. Clustering generates hierarchical, relational, and metric data, which are represented in a single visualization. • User-defined diagrams. With this technique the user investigates state transition graphs with custom diagrams. Diagrams are parameterized by linking their graphical properties to the data. Diagrams are integrated in a number of correlated visualizations. • Multiple views on traces. System traces are linear paths in state transition graphs. This technique provides the user with different perspectives on traces. • Querying nodes and edges. Direct manipulation enables the user to interactively inspect and query state transition graphs. In this way relations and patterns can be investigated based on data associated with nodes and edges. This dissertation shows that interactive visualization can play a role during the analysis of state transition graphs. The ability to interrogate visual representations of such graphs allows users to enhance their knowledge of the modeled systems. It is shown how the above techniques enable users to answer questions about their data. A number of case studies, developed in collaboration with system analysts, are presented. Finally, solutions to challenges encountered during the development of the visualization techniques are discussed. Insights generic to the field of visualization are considered and directions for future work are recommended
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient
status and characterize progression patterns from longitudinal health records.
One approach for disease progression modeling is to describe patient status
using a small number of states that represent distinctive distributions over a
set of observed measures. Hidden Markov models (HMMs) and its variants are a
class of models that both discover these states and make inferences of health
states for patients. Despite the advantages of using the algorithms for
discovering interesting patterns, it still remains challenging for medical
experts to interpret model outputs, understand complex modeling parameters, and
clinically make sense of the patterns. To tackle these problems, we conducted a
design study with clinical scientists, statisticians, and visualization
experts, with the goal to investigate disease progression pathways of chronic
diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's
disease, and chronic obstructive pulmonary disease (COPD). As a result, we
introduce DPVis which seamlessly integrates model parameters and outcomes of
HMMs into interpretable and interactive visualizations. In this study, we
demonstrate that DPVis is successful in evaluating disease progression models,
visually summarizing disease states, interactively exploring disease
progression patterns, and building, analyzing, and comparing clinically
relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic
Development Of Information Visualization Methods For Use In Multimedia Applications
The aim of the article is development of a technique for visualizing information for use in multimedia applications. In this study, to visualize information, it is proposed first to compile a list of key terms of the subject area and create data tables. Based on the structuring of fragments of the subject area, a visual display of key terms in the form of pictograms, a visual display of key terms in the form of images, and a visual display of data tables are performed. The types of visual structures that should be used to visualize information for further use in multimedia applications are considered. The analysis of existing visual structures in desktop publishing systems and word processors is performed.To build a mechanism for visualizing information about the task as a presentation, a multimedia application is developed using Microsoft Visual Studio software, the C# programming language by using the Windows Forms application programming interface. An algorithm is proposed for separating pieces of information text that have key terms. Tabular data was visualized using the “parametric ruler” metaphorical visualization method, based on the metaphor of a slide rule.The use of the parametric ruler method on the example of data visualization for the font design of children's publications is proposed. Interaction of using the method is ensured due to the fact that the user will enter the size of the size that interests for it and will see the ratio of the values of other parameters. The practical result of the work is the creation of a multimedia application “Visualization of Publishing Standards” for the visualization of information for the font design of publications for children. The result of the software implementation is the finished multimedia applications, which, according to the standardization visualization technique in terms of prepress preparation of publications, is the final product of the third stage of the presentation of the visual for
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
Improving Usability of Interactive Graphics Specification and Implementation with Picking Views and Inverse Transformations
Specifying and programming graphical interactions are difficult tasks,
notably because designers have difficulties to express the dynamics of the
interaction. This paper shows how the MDPC architecture improves the usability
of the specification and the implementation of graphical interaction. The
architecture is based on the use of picking views and inverse transforms from
the graphics to the data. With three examples of graphical interaction, we show
how to express them with the architecture, how to implement them, and how this
improves programming usability. Moreover, we show that it enables implementing
graphical interaction without a scene graph. This kind of code prevents from
errors due to cache consistency management
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems
Visual query systems (VQSs) empower users to interactively search for line
charts with desired visual patterns, typically specified using intuitive
sketch-based interfaces. Despite decades of past work on VQSs, these efforts
have not translated to adoption in practice, possibly because VQSs are largely
evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we
collaborated with experts from three diverse domains---astronomy, genetics, and
material science---via a year-long user-centered design process to develop a
VQS that supports their workflow and analytical needs, and evaluate how VQSs
can be used in practice. Our study results reveal that ad-hoc sketch-only
querying is not as commonly used as prior work suggests, since analysts are
often unable to precisely express their patterns of interest. In addition, we
characterize three essential sensemaking processes supported by our enhanced
VQS. We discover that participants employ all three processes, but in different
proportions, depending on the analytical needs in each domain. Our findings
suggest that all three sensemaking processes must be integrated in order to
make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25
in Vancouver, Canada. Paper will also be published in a special issue of IEEE
Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS
(InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing,
Visualization, Visualization design and evaluation method
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
Rich Counter-Examples for Temporal-Epistemic Logic Model Checking
Model checking verifies that a model of a system satisfies a given property,
and otherwise produces a counter-example explaining the violation. The verified
properties are formally expressed in temporal logics. Some temporal logics,
such as CTL, are branching: they allow to express facts about the whole
computation tree of the model, rather than on each single linear computation.
This branching aspect is even more critical when dealing with multi-modal
logics, i.e. logics expressing facts about systems with several transition
relations. A prominent example is CTLK, a logic that reasons about temporal and
epistemic properties of multi-agent systems. In general, model checkers produce
linear counter-examples for failed properties, composed of a single computation
path of the model. But some branching properties are only poorly and partially
explained by a linear counter-example.
This paper proposes richer counter-example structures called tree-like
annotated counter-examples (TLACEs), for properties in Action-Restricted CTL
(ARCTL), an extension of CTL quantifying paths restricted in terms of actions
labeling transitions of the model. These counter-examples have a branching
structure that supports more complete description of property violations.
Elements of these counter-examples are annotated with parts of the property to
give a better understanding of their structure. Visualization and browsing of
these richer counter-examples become a critical issue, as the number of
branches and states can grow exponentially for deeply-nested properties.
This paper formally defines the structure of TLACEs, characterizes adequate
counter-examples w.r.t. models and failed properties, and gives a generation
algorithm for ARCTL properties. It also illustrates the approach with examples
in CTLK, using a reduction of CTLK to ARCTL. The proposed approach has been
implemented, first by extending the NuSMV model checker to generate and export
branching counter-examples, secondly by providing an interactive graphical
interface to visualize and browse them.Comment: In Proceedings IWIGP 2012, arXiv:1202.422
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