Bar charts are widely used for their popularity. However, their effectiveness diminishes in datasets containing outliers, which compresses smaller values and hinders accurate interpretation. We present two implementations: a 2.5D version that simulates depth using 2D primitives and a 3D version with volumetric rendering. We conducted a user study with 15 participants to evaluate their effectiveness by comparing the proposed techniques with default bar charts across seven analytical tasks. These tasks encompassed four key categories: identification of extremes, value determination, estimation of ratios, and scale and growth analysis. The results indicate that perspective-based techniques may offer perceptual advantages in tasks, such as value identification and interval estimation. Subjective evaluations also suggest a favorable perception of new approaches. These findings can help practitioners select an appropriate visualization strategy when dealing with data that contain extreme values.

This study investigated the use of vibration duration as a parameter for encoding discrete quantitative data in nonvisual contexts. The primary objective was to develop and evaluate a perceptually calibrated vibrotactile scale that supports data-analysis tasks. The experimental methodology was adopted in two phases. The first involved a perceptual study using a three-alternative forced choice (3-AFC) method to identify just-noticeable differences (JND) associated with vibration duration. Based on these limits, a linear scale of discriminable patterns was constructed. In the second phase, the scale was applied to a user experiment involving three data analysis tasks: identification of the smallest, largest, and reference values. The results showed perceptual accuracy (0.82) in the reference-based task and revealed that duration differences with durations ≤200 ms exhibited a lower performance and reduced accuracy. These findings demonstrate that duration-based encoding is a viable strategy and highlight the importance of designing scales based on perceptual thresholds for interactive haptic systems.

This study aims to review academic research publications on techniques that deal with the shortcomings of classic information visualization charts. This review used snowballing to capture the academic papers and applied a filter to focus on articles that developed a new technique, resulting in 28 articles. We proposed some research questions and a taxonomy to classify the papers and conduct a detailed analysis of each study. In this way, the study identified one solution proposal per article that makes use of one or more of the ten (10) visual shortcomings mitigation techniques found for each of the five (5) problems related to Visual shortcomings that affect one of the five (5) information visualization techniques. As a result, we found solutions and mapped them to each of the identified visual shortcomings, including overlap, outliers, categorical occlusion, misuse of space, and non-perceptible elements. These solutions help to minimize the problem as a whole or in part, making it easier for the user to understand the information in the graphs. This article characterizes an initial effort to study academic papers that solved or mitigated shortcomings in classical information visualization charts.

Data coding and decoding are essential steps in communicating and interpreting information. These encodings are basic units of data representation, which can be combined and are generally organized by a layout of a data visualization technique, a common scenario for digital data visualizations. Technological advances in manipulating materials allowed us to represent data physically, creating an opportunity to use other senses, in addition to vision and hearing, to encode and decode data, such as touch, smell, and taste. In everyday life, touch is already widely used to decode information with few values using vibrations, such as turning devices on/off, communicating alerts on devices, and providing feedback in interaction in video games, among others. Still, it is generally not used to decode a larger set of values. Therefore, this article aims to investigate the encoding and decoding of data through tactile vibrations, identifying the minimum and maximum limits of tactile perception without causing discomfort and minimum intervals that differentiate two consecutive vibration values. This preliminary information allowed us to understand how to map data using vibrotactile scales. The data are mapped according to their value, changing the vibration frequency of the motors. Two stages of testing were carried out. The first aims to find the limits of tactile perception and create the vibrotactile scale, and the second aims to perform data analysis tasks, such as comparison, ordering, and identification. The results demonstrate that users considered find minimum and maximum vibration values tasks easy, while clustering and sorting tasks were classified as the most complex.

This paper aims to present the design process of dynamic data physicalization for bar charts and their variants, simple bars, stacked bars, and clustered bars. The physical artifact has 12 bars that are automatically configured according to the dataset by electromechanical components controlled by an Arduino board. The models of the printed 3D parts, the electromechanical components used and their connection scheme, the design aspects to set up the dynamic physical visualization according to the data, how the users choose the dataset and the kind of physical chart, usage scenarios with different types of charts and physicalization parameters are presented in detail. Finally, some strengths and difficulties in creating dynamic physical visualizations are highlighted and future works are proposed.

Even with the growth of digital data sources and support for creating visualizations, much information disseminated in chart format is still static, whether printed or digital. Some common problems of visualization components can be easily corrected on the chart design using a charting tool, but in the real world, mainly in the printed media, it is not a simple task. We investigate a method to reconstruct visualizations in real-time and in a real-world environment using Augmented Reality. We propose a prototype that interacts with extracted data from a bitmap chart in the real world, and we evaluated it with usability experts. The results show a high degree of satisfaction on many factors, mainly filter usage and valuable feedback about interaction.

This paper presents a proposal for text visualization with a focus on the representation of sentences and sentiments. It is noteworthy that traditional text visualization techniques, such as wordcloud, do not visually present relations between words, making it challenging to analyze textual data more deeply through visualization. EmojiText uses a directed graph, where the nodes represent the words and the edges represent the sequence in which the words appear in the sentences. Furthermore, the area of each node is related to the frequency of the represented word, and each node is associated with an emoji that represents the most frequent sentiment associated with the word. Finally, the edges also allow identifying the sentiment at the sentence level. The tool also includes zoom interactions, filters, details on demand, visual mapping configuration, and temporal analysis functionality through animations and multiple views for comparisons. The tool was developed in a javascript programming language using the d3.js framework and is available at http://emojitext.roberto.eti.br/.

This work aims to review the academic literature on information visualization techniques used in dashboards applied to strategic business or management decision-making in different application areas. The review used the snowballing method to obtain academic works and applied a filter to focus on papers published in journals or conference proceedings, reaching 44 papers. We propose four research questions and one taxonomy to classify the works, carry out the analyses, and later a research agenda to address the identified gaps. For instance, this review revealed a lack of academic papers that discuss the subject involving state-of-the-art information visualization and machine learning techniques. This paper represents our initial effort to examine academic works that use information visualization techniques and theory applied to dashboards that support decision-making in business or management areas.

With vast amounts of data, comes vast numbers of problems. The process of collecting data is far from perfect, either due to human factors or technological errors, which can lead to inaccuracies and uncertainties in the data. One such issue is missing data: the absence of information. Several methods can deal with missing values, but to choose the correct approach, it is necessary to diagnose the missing data mechanisms, which describe how the distribution of missingness in a given data variable correlates to other variables. This diagnosis can be made with statistical tests or data visualization techniques. However, statistical tests provide an uncertainty estimation that is often misinterpreted, and the visualizations readily available in data analysis packages have some scalability issues, such as cognitive overload and lack of screen space. Thus, this paper proposes a visual-interactive idiom for diagnosing missing data mechanisms. The proposed solution consists of a set of visual encodings and two derived metrics that synthesizes the missing data mechanisms and the uncertainty associated with this synthesis. We present the concepts behind the visual encodings, derived metrics, and interactions of the idiom.

The use of glyphs in data visualization aims to create a visual object that represents a data set through a set of graphic representations. Even though glyphs can help visualization techniques increase visual data representation, this approach sometimes can suffer from large amounts of data, making the glyph susceptible to perception errors of its visual variables. The grids and geomaps layouts are the most common layouts used with glyphs. However, when a more complex arrangement is considered, such as a treemap layout, the problem is even more challenging, as the small and variable areas directly influence glyph readability. Thus, this article proposes a summarization glyph to replace a set of treemap items whose areas are tiny, and that consequently would not be suitable for a glyph of their own. The proposed glyph enables data analysts to have some useful insights about the data in the overview exploration. The summarization glyph is a stacked bar chart representing the data dimensions of the tiny items. A decision tree trained on user perception tests selects the glyphs with low readability, deciding if they are kept, or included in the summarization glyph.

The use of objects for generating virtual content is increasingly present in the daily life of computer user. This form of interaction, called tangible interfaces, generates ways of visualizing data in different ways, such as Augmented Reality, which with the help of a given marker can present the user with a specific content. Thus, this work aims to demonstrate a prototype, which through cards as tangible objects, create, manipulate and interact with visualization techniques. Presenting the architecture developed for the development of the prototype, as well as the interaction approach according to the tasks of InfoVis. The prototype also seeks to offer collaboration among users, diversifying and facilitating learning about concepts and InfoVis.

Evaluating User Experience (UX) is not a trivial task, and UX specialists have used a variety of tools to ana- lyze data collected from user tests, which causes difficulty in synchronizing the data. This paper presents UXmood, a tool that condenses multiple distinct data types (audio, video, text, and eye-tracking) in a dashboard of coordinated visualizations to ease the analysis process and allow to manage several projects where each project has several logs of user interaction. The tool replays sessions of tests and uses a combination of different sentiment analysis techniques to present a suggestion of user sentiment at any given time during the tasks. The visualizations support brushing and details-on-demand interactions and are synchronized with a temporal slider, allowing analysts to see specific moments of the tests freely. Also, the uses of the sentiment analysis in the collected data may improved the qualitative analysis of UX.

The advent of Smart City and Big Data have generated many data on demographical factors, such as health, public transportation, basic sanitation, education, among others. However, this growing volume of data is little related to their demographic contexts, and when maps represent these data, they generally do not have the interactivity to allow exploration, either by changing the studied territory, increasing or decreasing its scope, relating other data for analysis, visually encoding a data, among other possibilities. Thus, this work aims to present a system that represents demographic data, specifically in geographic maps, and how to make it interactive for the user, presenting the software ChoroLibre as a final product: an opensource tool for visualizing hierarchical and interactive Choropleth geographic maps for the Web. To this end, this paper describes the architecture, implementation, methodology, technologies, and the developed functionalities of the ChoroLibre tool.

We introduce SongVis, a visualization that represents music's semantic descriptors. SongVis uses emojis, colors, lines and shapes to embody the semantic content of a song. It aims to aid users on tasks related to exploration/browsing of music libraries and at queries for similar tracks based on visual characteristics. We first collected the descriptors after surveying papers on the topic of "music visualization", and used a questionnaire to rank the terms by consulting the public. Then, the features: mood, danceability, tempo, music genre and instrument, were extracted using state-of-the-art music information retrieval algorithms and we considered their visuals. Then, we discuss potential improvements to be made. With SongVis we perform a step forward towards visually representing semantic descriptors and expect that the music information research, the information visualization fields and the public can benefit from this work.

The analysis of oceanographic data is a difficult task due to the large volume and multidimensionality of the data. Often is it necessary to aggregate other datasets to establish relationships and more complex analysis in order to answer environmental questions. Thus, this work aims to present different information visualization techniques (InfoVis) for oceanographic data concerning the most frequently used techniques, such as 2D and 3D Charts, Surface Map, Choropleth Map, and Line Map. For this purpose, a dataset of oceanographic buoys from the National Oceanic and Atmospheric Administration (NOAA) covering a period of 02 years (from 2015 to 2016) was used. This set of data was treated and classified according to the seasonal periods (winter, spring, summer, and autumn). The Rose Wind technique revealed that the wind speed average was between 10-12 m/s. The Circle Parking technique showed a predominance of wind and waves from NE. In 2015, highest waves were recorded, mainly during the winter. The obtained results can be considered a useful tool for the assessment of the wind and waves relationship and help to coastal management strategies.

There has been an increasing demand to understand and describe Evolutionary Computing techniques. Information Visualization may contribute with interactive data visualizations that help explore the population of individual solutions over the data search space and generations, convergent behavior, individual fitness, the dynamic of the evolutionary process among other possible scenarios. Although there are previous works on the use of visualization to analyze evolutionary techniques, there has been little diversity among the approached visualization techniques. Also, most related works consider only genetic algorithms and ignore other evolutionary approaches. Therefore the goal of this paper is to suggest the appropriate InfoVis techniques for the analyzed scenarios to better understand the behavior of evolutionary computing algorithms. Furthermore, we present a case study that applies the proposed scenarios to AutoClustering, a tool based on Estimation of Distribution Algorithms. We hope the proposed scenarios and techniques provide a set of good practices for the analysis of Evolutionary Computing techniques.

Element overlap in visualization techniques is a known problem, and high amounts of data and lack of available visual space potentialize this issue. Many studies have applied techniques to reduce occlusion levels in data visualizations, such as random jitter, element transparency, layout rearrangement, and focus+context techniques. However, few studies focus on the presence of occlusion, which is a relevant topic for visualizations where some degree of overlap is inevitable or purposefully explored. This paper takes a step in this direction, and presents a comparative study of visual variables, measuring their robustness to overlap and number of unique values. The study used a grid layout to display visual variables (hue, saturation, shape, text, orientation, and texture), and varied percentage of occlusion (0%, 50%, 60% and 70%) and number of unique values (3, 4 and 5) to measure the effect they cause on the speed and accuracy to locate the visual variables. Hence, 48 volunteers performed locate tasks on a tool that automatically generate a grid of visual variables and collect their answers. The results revealed that hue and shape were robust to high occlusion levels and a high number of unique values. Text and texture had medium loss of performance, while saturation and orientation were the most negatively affected.

Many information visualization techniques map abstract data into visual representations to present the underlying information in a more understandable way. However, when data is not available, automatic methods are necessary to extract the underlying data from the chart images. This paper focuses on chart image classification, which is a fundamental step of chart analysis and data extraction. The performance of most conventional classifiers depends on the feature extraction technique applied, usually requiring a domain-specific design for creating the features. In this context, Convolutional Neural Network (CNN) has gained attention not only because it has achieved good results in many computer vision tasks but also because it can learn representations of images without the design of a feature extractor specific for a domain problem. Thereby, this paper proposes an evaluation of CNN architectures (VGG19, Resnet-50, and Inception-V3), comparing them with conventional classifiers (HOG features combined with KNN, Naive Bayes, Random Forest and Support Vector Machine). An artificially generated dataset of 10 classes of chart images was used for training and testing. Furthermore, another dataset composed of chart images collected from the internet was used for validation. The results show that Resnet-50 and Inception-V3 performed best achieving an accuracy of 77.76% and 76.77% respectively on the validation set. The CNN models outperformed the conventional methods, where the best combination was HOG+SVM, achieving 45.03 %. Moreover, some similar chart types were expected to be misclassified, and they were discussed based on the confusion matrix.

The scarcity of chart images public datasets and its generators makes difficult the comparative studies between works in areas such as information visualization, usability, computer vision, and machine learning. Therefore, this paper presents a Web tool for generating data chart images called Synthetic Chart Image Generator (SCIG). The tool uses VEGA declarative language, and the chart features are fully parameterizable for every eleven different classes, as well as image quantity and its resolution. Additionally, the charts are constructed from synthetic data, randomly generated by probability distributions functions, and rendered in PNG format. Finally, this paper presents a performance test of the chart images generation.

The evaluation is an essential step of works that propose new information visualization techniques or tools. A common type is the controlled experiment in which the researchers measure the user performance to execute specific tasks using the proposed method. Furthermore, the dataset used for these tests must contain known desired features to be evaluated (e.g., level of noise, the percentage of missing values) in a controlled way. Thus, this article proposes an application to generate synthetic databases for evaluating information visualization techniques and tools. The system aims to create a dataset generator model that allows the construction of datasets with a diversity of profiles in a controlled manner. The creator of the model can save it for future experiments or updates and can export it enabling other groups to replicate the experiments easily. For a better understanding of application features and how to use it, this work also shows two use scenarios explaining the created model for each situation.

Water is one of the most precious assets on the planet, being essential for life and used for a wide range of purposes, such as irrigation of crops, power generation, navigation and transportation of cargo and passengers, among other activities. In the context of cargo handling, ports in Brazil have increased in importance at both national and international levels. In this sense, this work aims to propose a visual analysis of the physical-chemical parameters of the surface water to evaluate the influence of the port activity on its quality, using visualization techniques such as treemap, parallel coordinates, scatter plot, and histogram. The scenario evaluated corresponds to water around port facilities of Belém, Miramar and Vila do Conde, which are managed by Companhia Docas do Pará, located in the northern region of Brazil, in the state of Pará. The water quality assessment guideline will have as a basis the influence of the cargo with a higher volume of movement, the anthropic actions near the port, and Brazilian environmental legislation. The results indicate that the port activity has no relevant impacts on the quality of surface water compared to the launch of domestic sewage without adequate treatment near the port units and that the excessive rains potentiate this scenario.

Treemap is one of the most well-known and used techniques for data analysis in InfoVis. However, it still presents some challenges concerning data representation, such as small items, a large number of hierarchies limiting the visual space, few options for visual data representation (restricted to size, color, label), and others. Thus, this paper presents an InfoVis tool that allows the analysis of multidimensional data using treemaps with glyphs, once they represent more data visually combined visual variables. Besides that, considering treemap scenarios with small area items, an algorithm was developed to analyze which part of the glyph the application should draw since parts of the glyph can still provide useful information. In this way, the glyphs become adaptative to available space. The application has a multilabel decision tree technique that decides which part of the glyph should appear. Visualization specialists supplied the training data through a system that showed a diverse range of glyphs' representations. The system varied the glyph's size, the number and the value of the visible visual variables and registered the response of specialists in the training data. Finally, this paper presents images using the treemap with adaptive glyphs approach versos treemap+glyphs and showed that the adaptive approach clears information clutter when treemap items are small.

Different information visualization techniques can be found in the literature due to the quantity and variety of data stored in computational systems. In this context, the classification of chart images becomes important because it allows various types of graphs to be detected automatically in different contexts, allowing a more specific processing for each type of visualization, for example, data extraction. Several techniques of image classification can be used, where the most common are based on the extraction of features of the images, and a later classification using these features. However, one technique that has been gaining prominence in the context of image classification is the Convolutional Neural Network (CNN). This technique is based on deep learning and, in a way, encapsulates the feature extraction process. In this way, the proposal of this article is to use an architecture of a client-server based model to do the chart image classification and later data extraction from this image. The main advantage is doing the CNN processing on the server side, so the application does not rely on client device limitations. For this, an image dataset was generated from the web, and it has ten classes of graphs. From the experiments done, it was seen that the use of this technique was feasible, and modifications in the architecture can be made as a proposal to improve the accuracy of the model.

This paper proposes an edge bundling approach applied on parallel coordinates to improve the visualization of cluster information directly from the overview. Lines belonging to a cluster are bundled into a single curve between axes, where the horizontal and vertical positioning of the bundling intersection (known as bundling control points) to encode pertinent information about the cluster in a given dimension, such as variance, standard deviation, mean, median, and so on. The hypothesis is that adding this information to the overview improves the visualization overview at the same that it does not prejudice the understanding in other aspects. We have performed tests with participants to compare our approach with classic parallel coordinates and other consolidated bundling technique. The results showed most of the initially proposed hypotheses to be confirmed at the end of the study, as the tasks were performed successfully in the majority of tasks maintaining a low response time in average, as well as having more aesthetic pleasing according to participants' opinion.

This paper presents a starting study on InfoVis interaction through mid-air gestures, using a vision based interaction device, the Leap Motion. We present the user tests conducted and the results gathered, using a 3D Scatterplot as visualization technique. These tests aim in identify and categorize issues that can compromise InfoVis mid-air gestural interaction as a whole, not focusing on the design of the developed tool. The test tasks and results are exposed and the issues found are categorized in Boundary Awareness, Granularity and Collision and Depth Perception.

This paper presents results of head tracker and speech commands interaction evaluation in information visualization context at low level task taxonomy. The head tracker is used to move a pointer and the voice commands to perform actions. In the evaluation, we used 20 public information visualization techniques, 62 simple tasks (aiming low-level interactions), and 10 participants performed all tasks. The evaluation shows results about user performance (UP) and user experience (UE) by visualization and interaction taxonomies. Finally, we highlight some good practices suggestions for design of this type of interaction.

Design and develop collaborative methods in information visualization are a current challenge, hence understand, identify and define the current progress of developed methods to collaborate in information visualization are important. The aim of this work is to review the ways and strategies of how to collaborate in information visualization applications. We surveyed published works that present applications that implements collaborative information visualization techniques and methods. We present a micro level category of identified ways of how the InfoVis applications implements collaboration.

The current Brazilian context in television audiovisual communication is in transition from analogical to digital technology. One of the main reasons for this motion towards Interactive Digital Television (iDTV) is the improvement in the user experience over this platform. Considering this motion, this work applied suitable interaction design and usability concepts from the Nielsen's heuristics [5] into a visualization prototype to the support of visual analysis of products in electronic commerce over TV (t-commerce). In this work we conducted a usability check in an existing information visualization prototype, in order establish whether or not it follows usability standards and in order to make it more usable to the user.

This paper identifies an opportunity to reduce the latency in information visualization (InfoVis) systems, exploring the parallelization of the visualization pipeline architecture. We propose a concurrent architecture where the visualization pipeline stages are modified to execute as producers and consumers threads. The threads synchronization is done by memory barriers and the data flow pass the pipeline through a unique data structure, called ring buffer, which reuses a contiguous space preallocated in memory. Two InfoVis prototypes were developed in java, the first one using sequential pipeline and the other using concurrent pipeline. The results obtained with concurrent architecture in comparison with sequential pipeline presented less execution time and memory allocation for data visualization renderization.

The Internet has played an important role as a knowledge-sharing network and in this context some service oriented architecture (SOA) applications have emerged in all kind of study fields. Therefore, this work aims the design and development of a service aggregation that will favor ubiquity and pervasiveness in data visualization applications, allowing users to build domain-specific data visualizations in an easy and intuitive way. With this kind of service, it is possible to build data visualization applications for different smart devices such as smartphones, tablets, desktop, smart TV's, etc. A Web API that supports the main functionalities of an information visualization tool in different platforms has been proposed to reaches these purposes. The REST (Representational State Transfer) Style has been employed in the service conception as the architectural communication model. Client-side and server-side applications were developed using Java with a data visualization generator engine called PRISMA.