Information

Research

24 pages, 1655 KiB

Open AccessArticle

Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series

by Leonardo Christino, Martha D. Ferreira and Fernando V. Paulovich

Information 2022, 13(8), 368; https://doi.org/10.3390/info13080368 - 2 Aug 2022

Cited by 4 | Viewed by 1772

Abstract

Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in [...] Read more.

Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users’ visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder’s line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations. Full article

(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)

► Show Figures

Figure 1

16 pages, 5478 KiB

Open AccessArticle

Visualization of WiFi Signals Using Programmable Transfer Functions

by Alexander Rowden, Eric Krokos, Kirsten Whitley and Amitabh Varshney

Information 2022, 13(5), 224; https://doi.org/10.3390/info13050224 - 26 Apr 2022

Cited by 2 | Viewed by 3150

Abstract

In this paper, we show how volume rendering with a Programmable Transfer Function can be used for the effective and comprehensible visualization of WiFi signals. A traditional transfer function uses a low-dimensional lookup table to map the volumetric scalar field to color and [...] Read more.

In this paper, we show how volume rendering with a Programmable Transfer Function can be used for the effective and comprehensible visualization of WiFi signals. A traditional transfer function uses a low-dimensional lookup table to map the volumetric scalar field to color and opacity. In this paper, we present the concept of a Programmable Transfer Function. We then show how generalizing traditional lookup-based transfer functions to Programmable Transfer Functions enables us to leverage view-dependent and real-time attributes of a volumetric field to depict the data variations of WiFi surfaces with low and high-frequency components. Our Programmable Transfer Functions facilitate interactive knowledge discovery and produce meaningful visualizations. Full article

(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)

► Show Figures

Figure 1

21 pages, 17460 KiB

Open AccessArticle

Combining 2D and 3D Visualization with Visual Analytics in the Environmental Domain

by Milena Vuckovic, Johanna Schmidt, Thomas Ortner and Daniel Cornel

Information 2022, 13(1), 7; https://doi.org/10.3390/info13010007 - 27 Dec 2021

Cited by 11 | Viewed by 5948

Abstract

The application potential of Visual Analytics (VA), with its supporting interactive 2D and 3D visualization techniques, in the environmental domain is unparalleled. Such advanced systems may enable an in-depth interactive exploration of multifaceted geospatial and temporal changes in very large and complex datasets. [...] Read more.

The application potential of Visual Analytics (VA), with its supporting interactive 2D and 3D visualization techniques, in the environmental domain is unparalleled. Such advanced systems may enable an in-depth interactive exploration of multifaceted geospatial and temporal changes in very large and complex datasets. This is facilitated by a unique synergy of modules for simulation, analysis, and visualization, offering instantaneous visual feedback of transformative changes in the underlying data. However, even if the resulting knowledge holds great potential for supporting decision-making in the environmental domain, the consideration of such techniques still have to find their way to daily practice. To advance these developments, we demonstrate four case studies that portray different opportunities in data visualization and VA in the context of climate research and natural disaster management. Firstly, we focus on 2D data visualization and explorative analysis for climate change detection and urban microclimate development through a comprehensive time series analysis. Secondly, we focus on the combination of 2D and 3D representations and investigations for flood and storm water management through comprehensive flood and heavy rain simulations. These examples are by no means exhaustive, but serve to demonstrate how a VA framework may apply to practical research. Full article

(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)

► Show Figures

Figure 1

22 pages, 2254 KiB

Open AccessArticle

Cluster Appearance Glyphs: A Methodology for Illustrating High-Dimensional Data Patterns in 2-D Data Layouts

by Jenny Hyunjung Lee, Darius Coelho and Klaus Mueller

Information 2022, 13(1), 3; https://doi.org/10.3390/info13010003 - 23 Dec 2021

Cited by 4 | Viewed by 2976

Abstract

Two-dimensional space embeddings such as Multi-Dimensional Scaling (MDS) are a popular means to gain insight into high-dimensional data relationships. However, in all but the simplest cases these embeddings suffer from significant distortions, which can lead to misinterpretations of the high-dimensional data. These distortions [...] Read more.

Two-dimensional space embeddings such as Multi-Dimensional Scaling (MDS) are a popular means to gain insight into high-dimensional data relationships. However, in all but the simplest cases these embeddings suffer from significant distortions, which can lead to misinterpretations of the high-dimensional data. These distortions occur both at the global inter-cluster and the local intra-cluster levels. The former leads to misinterpretation of the distances between the various N-D cluster populations, while the latter hampers the appreciation of their individual shapes and composition, which we call cluster appearance. The distortion of cluster appearance incurred in the 2-D embedding is unavoidable since such low-dimensional embeddings always come at the loss of some of the intra-cluster variance. In this paper, we propose techniques to overcome these limitations by conveying the N-D cluster appearance via a framework inspired by illustrative design. Here we make use of Scagnostics which offers a set of intuitive feature descriptors to describe the appearance of 2-D scatterplots. We extend the Scagnostics analysis to N-D and then devise and test via crowd-sourced user studies a set of parameterizable texture patterns that map to the various Scagnostics descriptors. Finally, we embed these N-D Scagnostics-informed texture patterns into shapes derived from N-D statistics to yield what we call Cluster Appearance Glyphs. We demonstrate our framework with a dataset acquired to analyze program execution times in file systems. Full article

(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)

► Show Figures

Figure 1

17 pages, 6644 KiB

Open AccessArticle

Visual Active Learning for Labeling: A Case for Soundscape Ecology Data

by Liz Huancapaza Hilasaca, Milton Cezar Ribeiro and Rosane Minghim

Information 2021, 12(7), 265; https://doi.org/10.3390/info12070265 - 29 Jun 2021

Cited by 5 | Viewed by 2504

Abstract

Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models increases, it is crucial that [...] Read more.

Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models increases, it is crucial that more effective approaches to labeling are developed. In this paper, we report the development of a methodology and a framework to support labeling, with an application case as background. The methodology performs visual active learning and label propagation with 2D embeddings as layouts to achieve faster and interactive labeling of samples. The framework is realized through SoundscapeX, a tool to support labeling in soundscape ecology data. We have applied the framework to a set of audio recordings collected for a Long Term Ecological Research Project in the Cantareira-Mantiqueira Corridor (LTER CCM), localized in the transition between northeastern São Paulo state and southern Minas Gerais state in Brazil. We employed a pre-label data set of groups of animals to test the efficacy of the approach. The results showed the best accuracy at

94.58 %

in the prediction of labeling for birds and insects; and

91.09 %

for the prediction of the sound event as frogs and insects. Full article

(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)

► Show Figures

Figure 1

21 pages, 36591 KiB

Open AccessArticle

Quantitative and Qualitative Comparison of 2D and 3D Projection Techniques for High-Dimensional Data

by Zonglin Tian, Xiaorui Zhai, Gijs van Steenpaal, Lingyun Yu, Evanthia Dimara, Mateus Espadoto and Alexandru Telea

Information 2021, 12(6), 239; https://doi.org/10.3390/info12060239 - 3 Jun 2021

Cited by 8 | Viewed by 5396

Abstract

Projections are well-known techniques that help the visual exploration of high-dimensional data by creating depictions thereof in a low-dimensional space. While projections that target the 2D space have been studied in detail both quantitatively and qualitatively, 3D projections are far less well understood, [...] Read more.

Projections are well-known techniques that help the visual exploration of high-dimensional data by creating depictions thereof in a low-dimensional space. While projections that target the 2D space have been studied in detail both quantitatively and qualitatively, 3D projections are far less well understood, with authors arguing both for and against the added-value of a third visual dimension. We fill this gap by first presenting a quantitative study that compares 2D and 3D projections along a rich selection of datasets, projection techniques, and quality metrics. To refine these insights, we conduct a qualitative study that compares the preference of users in exploring high-dimensional data using 2D vs. 3D projections, both without and with visual explanations. Our quantitative and qualitative findings indicate that, in general, 3D projections bring only limited added-value atop of the one provided by their 2D counterparts. However, certain 3D projection techniques can show more structure than their 2D counterparts, and can stimulate users to further exploration. All our datasets, source code, and measurements are made public for ease of replication and extension. Full article

(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)

► Show Figures

Figure 1

22 pages, 3965 KiB

Open AccessArticle

Colvis—A Structured Annotation Acquisition System for Data Visualization

by Pierre Vanhulst, Raphaël Tuor, Florian Évéquoz and Denis Lalanne

Information 2021, 12(4), 158; https://doi.org/10.3390/info12040158 - 9 Apr 2021

Cited by 2 | Viewed by 2656

Abstract

Annotations produced by analysts during the exploration of a data visualization are a precious source of knowledge. Harnessing this knowledge requires a thorough structure of annotations, but also a means to acquire them without harming user engagement. The main contribution of this article [...] Read more.

Annotations produced by analysts during the exploration of a data visualization are a precious source of knowledge. Harnessing this knowledge requires a thorough structure of annotations, but also a means to acquire them without harming user engagement. The main contribution of this article is a method, taking the form of an interface, that offers a comprehensive “subject-verb-complement” set of steps for analysts to take annotations, and seamlessly translate these annotations within a prior classification framework. Technical considerations are also an integral part of this study: through a concrete web implementation, we prove the feasibility of our method, but also highlight some of the unresolved challenges that remain to be addressed. After explaining all concepts related to our work, from a literature review to JSON Specifications, we follow by showing two use cases that illustrate how the interface can work in concrete situations. We conclude with a substantial discussion of the limitations, the current state of the method and the upcoming steps for this annotation interface. Full article

(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)

► Show Figures

Figure 1

22 pages, 5383 KiB

Open AccessArticle

Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

by Muhammad Jawad, Jens Soltwisch, Klaus Dreisewerd and Lars Linsen

Information 2020, 11(12), 575; https://doi.org/10.3390/info11120575 - 9 Dec 2020

Cited by 1 | Viewed by 3684

Abstract

Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios ( [...] Read more.

Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (

m / z

-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual

m / z

-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images. Full article

(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Trends and Opportunities in Visualization and Visual Analytics

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI