Deep reinforcement learning is a dynamic field that has been successfully applied in various engineering and scientific disciplines. However, like many areas of automated learning, it presents a significant challenge: understanding its models, which can hinder human trust in the decisions made by these algorithms. To address this issue, we conducted a focus group and interviews with experts in machine learning and reinforcement learning to gain insights into the perceptions and preferences surrounding interpretability techniques and tools in reinforcement learning systems. The discussion of the focus groups used an interactive dashboard to simplify the analysis of reinforcement learning environments. The focus group results showed many problems to tackle with the current approaches regarding RL agent training, like data manipulation, environment representation and reward misinterpretation. The focus group discussion also presented some key features of a visualization approach to interpretability in RL: contextual presentation of information, integration with existing pipelines, and ease of distribution.

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 study presents Glyphforge, an automated visual encoding method that makes use of visual variables that partially overlap. The heuristics utilized to optimize the efficiency of the partial overlap factor were derived from studies centered around visual perception assessments. By combining preprocessing and feature selection techniques, this approach aims to identify and retain a maximum of five key features from a given dataset. A visualization technique utilizing layered multidimensional data glyphs was implemented for the purpose of the visual encoding. This tool considers some use cases for the end users, like manipulation of columns and layers, and inverting the dimensions for the glyph layers.

Recent literature reviews point out that active data physicalizations still lack evaluation scenarios. These evaluations mainly focus on the perception of visual and physical characteristics of physicalizations and users' performance of analysis tasks. This paper addresses a proposed accuracy assessment of data physicalization for data representation. The data physicalization used is a physical and dynamic bar chart using 3D printed models (3D bars) and LED strips inside the bars, both controlled by microcontrollers (Arduino), being possible to represent different datasets. The accuracy assessment of this physicalization is based on the height and color of the 3D bars. For this, distance sensors measure the actual height values of the bars, and a computer vision camera/algorithm collects and estimates the RGB value of the bars. From original and measured values datasets, bar charts images are built to perform a similarity comparison between the images. The similarity metric is used as an accuracy metric of the bar heights to represent data and obtained as initial results value higher than 96%. Furthermore, an evaluation scenario of the distance between the reference and measured colors 2000) classifies most of them as having proper perception. Additionally, the accuracy loss was evaluated on data physicalization in a scenario of continuous use (ten configurations without any calibration), and the variation of errors was around 2%.

The value of good data visualization has already been shown in several scenarios. Still, it is not always easy to obtain it, as it depends on factors such as the dataset, the amount of data, task types, the user profile, the type of interaction, etc. To mitigate the challenges addressed, automated or semi-automated systems have been proposed, emphasizing rule-based/heuristic approaches and machine-learning models. However, many of these applications require specialized knowledge and present results (data visualizations) that are not flexible for customization. Papers have highlighted the ease of tools like ChatGPT in creating various tasks, including creating data charts. This facility, in addition to the intelligent computational model involved, is also due to the expressiveness used in the requests to execute the tasks by the users since these tools use Natural Language Interfaces. Despite adopting these tools overgrowing in different scenarios of society, studies on the best way to use them, integrate them into existing processes, or evaluative studies on their effectiveness or efficiency are still incipient. Thus, this paper will evaluate the workload for creating data visualization using ChatGPT 3.5. For assessment, the NASA Task Load Index (Nasa TLX) methodology was applied, and users with experience creating data visualization created two proposed scenarios. The preliminary results showed high temporal and mental demand, mainly due to the vocabulary used and the completeness of the user instructions. The average time to create and perform InfoVis tasks in two proposed evaluation scenarios was 33 and 44 minutes, and 14 queries were applied on average for both scenarios. The direct consequence was that the users have redone the requests and improved the instructions at each new iteration, and all users completed the proposed tasks.

In recent years, the physicalization area has expanded rapidly and has been utilized in numerous contexts, mostly in big open public areas or data-related places. Most data physicalizations have visual restrictions and no interaction. Hence, multiple technologies, including augmented reality (AR), have been applied to physical visualizations to address the above-mentioned issues. This research proposes dynamic data physicalization using AR virtual content (visual data items). The dynamic bar chart allows the configuration of numerical data to height, categorical data to color, and categorical data to the x-axis. The mobile augmented reality (MAR) application performs some Infovis tasks, such as settings, filters, details on demand, etc. A cloud server selects data, calculates visual elements or additional visualizations, calculates scales for physicalizing data, and enables the communication between the MAR application and dynamic physicalization. Lastly, dynamic and augmented data physicalization characteristics and scenarios are shown.

The process of creating physical data visualizations is not a trivial task. In general, it may require skills from the user in information visualization, tangible interaction, 3D modeling, fabrication of physical objects, etc. In addition, few works have presented computational support to the entire digital and physical rendering pipeline of data visualization, characterizing many steps of this process as manual. From this context, this work presents a process that facilitates the generation of physical data visualization. Besides that, It allows one to define which type of physical visualization to create, among passive, rearrangeable, and dynamic physicalization types. Finally, the pipelines for each physicalization type are presented, with scenarios including physical bar charts, stacked bar charts, and grouped bar charts.

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.

The textual description of data charts is a complex task. A chart presents different visual characteristics for the information represented, which can be influenced by the technique selected and the combination of visual elements. There are crowdsourcing initiatives to create descriptions for charts available on the Web, but the descriptions can have failures, considering that they arise from the understanding of the person. In this context, methods to automatically extract data from chart images allow producing descriptions for use in these scenarios. However, there is no standard way of vocalizing the chart content. For this, the textual description must be based on a template, so that the chart can be completely understood. Thus, this paper presents templates that allow verbalizing the data extracted from vertical and grouped bar charts in an intelligible way. Evaluations were performed with users to verify the ease of understanding textual descriptions. The results showed that the proposed templates were suitable for vocalization the contents of bar charts.

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.

Metagenomics can be seeing as a data sampling of microorganisms genome sequences inhabiting a common environment. Those sequences can further be evaluated to estimate the impact of the presence or absence of these microorganism in the environment. Thus, sequence data allows the identification of the taxonomic and functional signatures available in a environmental sample. These signatures are usually not explored in a integrated manner and the lack of proper environmental metadata limit the data analysis. In this paper we propose an integrated methodology to identify and compare metagenomic signatures, correlating them with environmental factors (when they are available). Given that the hierarchical (tree-structured) data solution are kind of a set of nested clusters, a Bubble Tree visualization (by Javascript) is also provided to guide the exploration of the metagenomic signatures. Additionally, it make it practical and an easy-to-use way to compare several metagenomic samples. As far as we are concerned such data and interactive visualization analysis has never been explored before.

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.

Many information visualization techniques and tools have been created to analyze and understand temporal data. However, it is noticeable that time dimension is underused in most techniques and tools, used only as an aid in the search for patterns and answers. Therefore, this paper proposes an adaptation of the treemap joined with the calendar metaphor, using time as main attribute to hierarchy configuration and drill down navigation. Time granularity is flexible on granularity levels, and a grouping configuration of one level is used to display the calendar visual representation of that same granularity level. Furthermore, user interaction breadcrumbs are applied in order to provide better linear analysis of temporal characteristics of data without losing data overview. It is also possible to compare multiple different views of various time subsets.

Water is one of the main resources of nature, being the element directly related with maintenance of life in any ecosystem. For mankind, beside the biologic needs, water is used in many sectors of society, and one of them is electricity generation. For the full operation of hydroelectric power plants there is a need to create a huge water reservoir, which transforms the ecosystem and local economy, serving multiple uses, such as tourism, fishing, recreation, sailing, among others. The maintenance in this context is dependent of the quality of water in reservoir, which justifies its continuous monitoring. From the monitored data, it is possible to predict or classify the hydrologic cycle for the reservoir, aiming to support the process of decision about the hydric resources and environmental impact. Thus, this paper presents a practical approach to use visualization techniques based on the PRISMA software, which is a visualization tool with features of multiples views and coordinated databases to identify patterns in hydrologic cycles, considering the psycho-chemical elements existent in the dam water of the hydroelectric power plant from Tucuruí - Pará - Brazil.

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.

This paper aims to minimize the problems encountered in the handling of large amounts of visual data, proposing a scalable environment for distributed applications and information visualization (IV) with multiple coordinated views. The benefits of this architecture are improving the processing power for visualization tools, reduce the rendering time of visualization techniques, reducing the problem of occlusion and aggregation, among others. We will be presented usability testing, and comparative testing of performance (rendering time, memory usage, and processor) with an application of IV in a single desktop.

Most of the existing Data Mining algorithms have been manually produced, that is, have been developed by a human programmer. A prominent Artificial Intelligence research area is automatic programming - the generation of a computer program by another computer program. Clustering is an important data mining task with many useful real-world applications. Particularly, the class of clustering algorithms based on the idea of data density to identify clusters has many advantages, such as the ability to identify arbitrary-shape clusters. We propose the use of Estimation of Distribution Algorithms for the artificial generation of density-based clustering algorithms. In order to guarantee the generation of valid algorithms, a directed acyclic graph (DAG) was defined where each node represents a procedure (building block) and each edge represents a possible execution sequence between two nodes. The Building Blocks DAG specifies the alphabet of the EDA, that is, any possibly generated algorithm. Preliminary experimental results compare the clustering algorithms artificially generated by AutoClustering to DBSCAN, a well-known manually-designed algorithm.

Digital TV interactivity implies a great potential for social and digital inclusion, especially in a country like Brazil, where 95% of households have at least one TV. Due to the importance of Interactive Digital TV in this scenario, it is necessary not only to provide new applications to support services on TV, but also to evaluate the technological challenges and development practices, in order to provide viewers suitable interactivity considering the environment limitations. This paper presents the design process of a t-commerce application for the Brazilian iDTV middleware, the theories that based the development and an analysis of its appliance in a real scenario test.

Multi-display environment (MDE) has evolved in recent years; however, the adjustment of common visualization system to MDEs poses several challenges. This paper describes an information visualization (IV) tool for a distributed and scalable environment with coordinated multiple views of data, which simulates an extended desktop across multi-display. This work is limited to the use of three monitors. This document presents satisfactory results of preliminary tests of usability. Performance tests showed an IV pipeline is more optimized than the common visualization tool.

The goal of this paper is to present PRISMA Mobile, an information visualization tool for android-based tablets. The application is based on a client-server model where the client uses as few resources as possible in order to preserve memory, battery and other device resources. To get better profit from the device, the tool uses the treemap technique coupled to other IV resources, as zoom, filters and details-on-demand, to improve user cognitive capabilities when analyzing data.

The goal of this paper is to present an information visualization application capable of opening and coordinating information over two or more datasets. We have chosen this approach to address some of the limitations of various applications. Also, the application uses multiple coordinated views and multiple simultaneous datasets. We highlight the application layout configuration by the user, including the flexibility to specify the number of data views and to associate different datasets for each visualization technique. Diferente approaches are suggested of how to deal and interact with multiple datasets.

Nowadays, workflow in various scenarios requires teamwork and collaboration. There is no doubt that working in teams and cooperate together provides a way to get better results in a short period of time compared to a single person job. With the large output of data, Information Visualization have become an important area to organize and assist decision making in many areas such as industry, medicine, education etc. In other to improve the goal of collaboration and decision making through alternative ways of interaction, this paper presents an augmented reality application for information visualization support.

Frequency-based statistical charts are the most widespread visualizations and the preferred of Infovis novices. They are important in depicting categorical data and providing data abstraction on large datasets. The drill down feature enables better visual scaling by allowing navigation through aggregation hierarchies. In this paper we discuss design considerations that impact on the usability in drill down charts. We develop a tool based on pie chart in order to illustrate the discussion. Moreover, we suggest visual representations and an interaction model. Finally, we provide evidence on the benefit of using such techniques for visual analysis of categorical data through a short usability study.

With the spread of sales in electronic commerce and the popularization of interactive devices for digital television, grows the demand for applications that supports services over this platform, considering that television devices are present in 95% of brazilian households. In order to supply this demand, this work proposes a visualization interface for visual analysis of products in electronic commerce, through the Interactive Digital TV, the t-commerce.

This paper presents a new view for PRISMA information visualization tool, this new visualization will provide support for analyzing data on maps. The use of maps will provide a way for geographical data analysis by using coordinates. Geographic analysis becomes important when it comes to verification of database that uses the location as a key factor to create contexts. This paper will describe the new integrated view and main features of the map.

In today's information systems, strategies for decision making and accurate results in a short time are crucial in many knowledge areas of industry, economic, medical etc.. To support this demand to store and interpret relevant information, the area of information visualization coupled with several others, such as intelligent systems and data mining, is increasingly taking place in academic research and market. Aiming to increase to level of interaction and accurate results, this article presents a collaborative augmented reality application for information visualization support.

A prominent Artificial Intelligence research area is automatic programming -- the generation of a computer program by another computer program. We propose the use of Estimation of Distribution Algorithms (EDA) for the artificial generation of density-based clustering algorithms -- a class of data mining algorithms. Preliminary experimental results compare the clustering algorithms artificially generated by AutoClustering to DBSCAN, a well-known manually-designed algorithm.

The goal of this paper is to present WebPrisma, a web information visualization tool still in development. The application supports multiple coordinated views including three visualization techniques - scatter plot, treemap and parallel coordinates – and auxiliary charts. WebPrisma supports view coordination by color, filter, selection, brushing and details on-demand. Standard web technologies and design patterns were applied.

The goal of this paper is to present a web based information visualization application with the use of Silverlight technology. We have chosen this technology to better use the processing power of the client application in order to support the use of advanced interactions and the same interface components of a desktop application.Also, the information visualization application uses multiple coordinated views and multiple simultaneous datasets. We highlight the application layout configuration by the user, including the flexibility to specify the number of data views and to associate different datasets for each visualization techniques.

Information Visualization techniques have been successfully used to build visual and interactive analysis of data and data relationships. One of its possible areas of application is computer network monitoring and traffic analysis. The analysis of the internal behavior of a company with high demand for Internet services helps to determine if the company is competing with its own clients over the available resources for online transactions, or event putting client information in risk. PRISMA, a tool with multiple coordinated data views has been analyzed to find patterns involving transactions against the company security policies. The goal is to suggest changes to improve the quality of service.

Getting to know clients and their behavior is essential in the growing process of any company that searches for improvement and business opportunities. One of the ways to discover more about their clientspsila behavior is to analyze the history of their transactions with the company or partners. The amount of transactions is often huge, what demands tools that help explore and analyze data in search for patterns or outliers. The tool used in this paper is called PRISMA and is based on multiple coordinated visualization techniques. Its major goal is to enhance the userpsilas perception about data and what is related to it, identifying problems that might occur during transactions, in order to improve or find business opportunities in them.

The use of information visualization tools that support multiple coordinated views for data exploration and analysis has become more frequent. However, few usability studies have focused this group of tools. The goal of this paper is to perform usability experiments with tools supporting multiple coordinated views on the same screen, in separate screens and tools that support each technique individually. To ensure a fair evaluation, a complexity-based task taxonomy was developed. The tools were therefore used to answer questions of the same level of complexity.

This paper presents a data analysis tool for mobile devices. Coordinated views are used to enhance the user's perception over data and diminish the difficulties caused by the lack of resources these mobiles present, specially when it comes to display size. The tool was totally developed in JavaME, what guarantees portability, scalability and code reuse. It presents two visualization techniques, implemented and coordinated with filters, scatterplot graph with georeferenced maps and treemap. Besides, preliminary usability tests were performed with users.

This paper presents EDACluster, an estimation of distribution algorithm (EDA) applied to the clustering task. EDA is an evolutionary algorithm used here to optimize the search for adequate clusters when very little is known about the target dataset. The proposed algorithm uses a mixed approach - density and grid- based - to identify sets of dense cells in the dataset. The output is a list of items and their associated clusters. Items in low-density areas are considered noise and are not assigned to any cluster. This work uses four public domain datasets to perform the tests that compare EDACluster with DBSCAN, a conventional density-based clustering algorithm.

The paper presents a prototype that implements multiple coordinated views to information visualization in augmented reality environments. Augmented reality provides more intuitive and easy interaction. It also reduces the limitations of visualization and enhances the collaborative aspect. A modified version of ARToolKit has been used to create the augmented environment, and the 3D scatter plot technique has been developed to represent the multiple views of data. The coordination between views supported dynamic queries, data selection, and the configuration of views and of the detail on demand feature. This paper also reproduces literature usability tests based on tasks in order to analyze the viability to implement multiple coordinated views in augmented environments for information visualization.

Information visualization techniques have been successfully used to build visual and interactive analysis of data and data relationships. However, dimensionality, volume and diversity make it difficult for a single technique to completely solve the problem. Information visualization tools that support multiple coordinated views allow the user to better understand the dataset by providing different perspectives based on the same set of data items. These tools support the correlation between data views and the coordinated real-time interaction mechanisms. The goal of this paper is to present the PRISMA information visualization tool that explores the use of multiple coordinated views. PRISMA provides usability, portability and extensibility. Initial usability essays are discussed.

This paper presents a prototype for information visualization in an Augmented Reality environment. The prototype allows users to perform common tasks in desktop information visualization tools, such as data dynamic filters, attribute selection, semantic zoom and details on demand. Such tasks are seldom available in AR environments. We used a customized version of ARToolKit, a software tool that allows the insertion of virtual objects in real environments through cards with marks. The interactive controls as well as the data visualization are projected to the user and manipulated through these cards.

The amount of data that must be collected for one to decide on a course of action, nowadays and in any field, is nothing if extraordinary. The three-dimensional graphic representation of data is a powerful tool for its analysis. And the possibility for interaction and browsing throughout these data makes this analysis much more interesting and productive. Nevertheless, one doesn't usually make a decision alone, and most tools are of a single-user nature, which jeopardizes the decision-making process, especially if those involved are distant from each other, geographically speaking. With a collaborative information visualization environment, it is possible to debate over the same data and change them if necessary, in real time, diminishing time and avoiding mistakes in the decision-making process. Therefore, this paper proposes a Web-based collaborative tool for data visualization using Java, EAI (external authoring interface) and VRML (Virtual Reality Modeling Language).