24,238 research outputs found
Approximated and User Steerable tSNE for Progressive Visual Analytics
Progressive Visual Analytics aims at improving the interactivity in existing
analytics techniques by means of visualization as well as interaction with
intermediate results. One key method for data analysis is dimensionality
reduction, for example, to produce 2D embeddings that can be visualized and
analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a
well-suited technique for the visualization of several high-dimensional data.
tSNE can create meaningful intermediate results but suffers from a slow
initialization that constrains its application in Progressive Visual Analytics.
We introduce a controllable tSNE approximation (A-tSNE), which trades off speed
and accuracy, to enable interactive data exploration. We offer real-time
visualization techniques, including a density-based solution and a Magic Lens
to inspect the degree of approximation. With this feedback, the user can decide
on local refinements and steer the approximation level during the analysis. We
demonstrate our technique with several datasets, in a real-world research
scenario and for the real-time analysis of high-dimensional streams to
illustrate its effectiveness for interactive data analysis
A tool for subjective and interactive visual data exploration
We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user’s belief about the data. In contrast, our generic tool computes data visualizations that are surprising given a user’s current understanding of the data. The user’s belief state is represented as a set of projection tiles. Hence, this user-awareness offers users an efficient way to interactively explore yet-unknown features of complex high dimensional datasets
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Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis
In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data
Vialactea Visual Analytics tool for Star Formation studies of the Galactic Plane
We present a visual analytics tool, based on the VisIVO suite, to exploit a
combination of all new-generation surveys of the Galactic Plane to study the
star formation process of the Milky Way. The tool has been developed within the
VIALACTEA project, founded by the 7th Framework Programme of the European
Union, that creates a common forum for the major new-generation surveys of the
Milky Way Galactic Plane from the near infrared to the radio, both in thermal
continuum and molecular lines. Massive volumes of data are produced by space
missions and ground-based facilities and the ability to collect and store them
is increasing at a higher pace than the ability to analyze them. This gap leads
to new challenges in the analysis pipeline to discover information contained in
the data. Visual analytics focuses on handling these massive, heterogeneous,
and dynamic volumes of information accessing the data previously processed by
data mining algorithms and advanced analysis techniques with highly interactive
visual interfaces offering scientists the opportunity for in-depth
understanding of massive, noisy, and high-dimensional data
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy
Three-dimensional clinical gait analysis is essential for selecting optimal
treatment interventions for patients with cerebral palsy (CP), but generates a
large amount of time series data. For the automated analysis of these data,
machine learning approaches yield promising results. However, due to their
black-box nature, such approaches are often mistrusted by clinicians. We
propose gaitXplorer, a visual analytics approach for the classification of
CP-related gait patterns that integrates Grad-CAM, a well-established
explainable artificial intelligence algorithm, for explanations of machine
learning classifications. Regions of high relevance for classification are
highlighted in the interactive visual interface. The approach is evaluated in a
case study with two clinical gait experts. They inspected the explanations for
a sample of eight patients using the visual interface and expressed which
relevance scores they found trustworthy and which they found suspicious.
Overall, the clinicians gave positive feedback on the approach as it allowed
them a better understanding of which regions in the data were relevant for the
classification.Comment: 7 pages, 4 figures; supplemental material 9 pages, 8 figures; to be
published in the proceedings of the 2022 IEEE Workshop on TRust and EXpertise
in Visual Analytics (TREX
Visual comparative case analytics
Criminal Intelligence Analysis (CIA) faces a challenging task in handling high-dimensional data that needs to be investigated with complex analytical processes. State-of-the-art crime analysis tools do not fully support interactive data exploration and fall short of computational transparency in terms of revealing alternative results. In this paper we report our ongoing research into providing the analysts with such a transparent and interactive system for exploring similarities between crime cases. The system implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed Visual Analytics (VA) workflow iteratively supports the interpretation of obtained clustering results, the development of alternative models, as well as cluster verification. The visualizations offer a usable way for the analyst to provide feedback to the system and to observe the impact of their interaction
Exploring Dimensionality Reduction Effects in Mixed Reality for Analyzing Tinnitus Patient Data
In the context of big data analytics, gaining insights into high-dimensional data sets can be properly achieved, inter alia, by the use of visual analytics. Current developments in the field of immersive analytics, mainly driven by the improvements of smart glasses and virtual reality headsets, are one enabler to enhance user-friendly and interactive ways for data analytics. Along this trend, more and more fields in the medical domain crave for this type of technology to analyze medical data in a new way. In this work, a mixed-reality prototype is presented that shall help tinnitus researchers and clinicians to analyze patient data more efficiently. In particular, the prototype simplifies the analysis on a high-dimensional real-world tinnitus patient data set by the use of dimensionality reduction effects. The latter is represented by resulting clusters, which are identified through the density of particles, while information loss is denoted as the remaining covered variance. Technically, the graphical interface of the prototype includes a correlation coefficient graph, a plot for the information loss, and a 3D particle system. Furthermore, the prototype provides a voice recognition feature to select or deselect relevant data variables by its users. Moreover, based on a machine learning library, the prototype aims at reducing the computational resources on the used smart glasses. Finally, in practical sessions, we demonstrated the prototype to clinicians and they reported that such a tool may be very helpful to analyze patient data on one hand. On the other, such system is welcome to educate unexperienced clinicians in a better way. Altogether, the presented tool may constitute a promising direction for the medical as well as other domains
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