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1.
Artigo em Inglês | MEDLINE | ID: mdl-36166544

RESUMO

We conduct a user study to quantify and compare user performance for a value comparison task using four bar chart designs, where the bars show the mean values of data loaded progressively and updated every second (progressive bar charts). Progressive visualization divides different stages of the visualization pipeline-data loading, processing, and visualization-into iterative animated steps to limit the latency when loading large amounts of data. An animated visualization appearing quickly, unfolding, and getting more accurate with time, enables users to make early decisions. However, intermediate mean estimates are computed only on partial data and may not have time to converge to the true means, potentially misleading users and resulting in incorrect decisions. To address this issue, we propose two new designs visualizing the history of values in progressive bar charts, in addition to the use of confidence intervals. We comparatively study four progressive bar chart designs: with/without confidence intervals, and using near-history representation with/without confidence intervals, on three realistic data distributions. We evaluate user performance based on the percentage of correct answers (accuracy), response time, and user confidence. Our results show that, overall, users can make early and accurate decisions with 92% accuracy using only 18% of the data, regardless of the design. We find that our proposed bar chart design with only near-history is comparable to bar charts with only confidence intervals in performance, and the qualitative feedback we received indicates a preference for designs with history.

2.
Artigo em Inglês | MEDLINE | ID: mdl-36155459

RESUMO

This article reports on an in-depth study that investigates barriers to network exploration with visualizations. Network visualization tools are becoming increasingly popular, but little is known about how analysts plan and engage in the visual exploration of network data-which exploration strategies they employ, and how they prepare their data, define questions, and decide on visual mappings. Our study involved a series of workshops, interaction logging, and observations from a 6-week network exploration course. Our findings shed light on the stages that define analysts' approaches to network visualization and barriers experienced by some analysts during their network visualization processes. These barriers mainly appear before using a specific tool and include defining exploration goals, identifying relevant network structures and abstractions, or creating appropriate visual mappings for their network data. Our findings inform future work in visualization education and analyst-centered network visualization tool design.

3.
IEEE Comput Graph Appl ; 42(4): 89-102, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34161239

RESUMO

We report on the process and design of our visual analytics graph analysis challenge winning entry. Specifically, our team addressed the IEEE VAST 2020 Mini-Challenge 1 that asked participants to identify a group of people that accidentally caused an internet outage. To identify this group, we were given a network profile and a large multivariate social network to search in. Our approach involved statistical and graphical analysis as well as the design of three custom visual analytics tools. The submitted solution and visualizations are available at https://graphletmatchmaker.github.io/.

4.
IEEE Comput Graph Appl ; 42(1): 84-94, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-33848242

RESUMO

We present BitConduite, a visual analytics approach for explorative analysis of financial activity within the Bitcoin network, offering a view on transactions aggregated by entities, i.e., by individuals, companies, or other groups actively using Bitcoin. BitConduite makes Bitcoin data accessible to nontechnical experts through a guided workflow around entities analyzed according to several activity metrics. Analyses can be conducted at different scales, from large groups of entities down to single entities. BitConduite also enables analysts to cluster entities to identify groups of similar activities as well as to explore characteristics and temporal patterns of transactions. To assess the value of our approach, we collected feedback from domain experts.


Assuntos
Comércio , Administração Financeira , Benchmarking , Humanos
5.
IEEE Trans Vis Comput Graph ; 28(1): 593-603, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34587089

RESUMO

We present a pyramid-based scatterplot sampling technique to avoid overplotting and enable progressive and streaming visualization of large data. Our technique is based on a multiresolution pyramid-based decomposition of the underlying density map and makes use of the density values in the pyramid to guide the sampling at each scale for preserving the relative data densities and outliers. We show that our technique is competitive in quality with state-of-the-art methods and runs faster by about an order of magnitude. Also, we have adapted it to deliver progressive and streaming data visualization by processing the data in chunks and updating the scatterplot areas with visible changes in the density map. A quantitative evaluation shows that our approach generates stable and faithful progressive samples that are comparable to the state-of-the-art method in preserving relative densities and superior to it in keeping outliers and stability when switching frames. We present two case studies that demonstrate the effectiveness of our approach for exploring large data.

6.
IEEE Trans Vis Comput Graph ; 27(7): 3135-3152, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-31899429

RESUMO

We present a systematic review of visual analytics tools used for the analysis of blockchains-related data. The blockchain concept has recently received considerable attention and spurred applications in a variety of domains. We systematically and quantitatively assessed 76 analytics tools that have been proposed in research as well as online by professionals and blockchain enthusiasts. Our classification of these tools distinguishes (1) target blockchains, (2) blockchain data, (3) target audiences, (4) task domains, and (5) visualization types. Furthermore, we look at which aspects of blockchain data have already been explored and point out areas that deserve more investigation in the future.

7.
IEEE Comput Graph Appl ; 41(2): 76-88, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33095705

RESUMO

We describe Cartolabe, a web-based multiscale system for visualizing and exploring large textual corpora based on topics, introducing a novel mechanism for the progressive visualization of filtering queries. Initially designed to represent and navigate through scientific publications in different disciplines, Cartolabe has evolved to become a generic framework and accommodate various corpora, ranging from Wikipedia (4.5M entries) to the French National Debate (4.3M entries). Cartolabe is made of two modules: The first relies on natural language processing methods, converting a corpus and its entities (documents, authors, and concepts) into high-dimensional vectors, computing their projection on the two-dimensional plane, and extracting meaningful labels for regions of the plane. The second module is a web-based visualization, displaying tiles computed from the multidimensional projection of the corpus using the Umap projection method. This visualization module aims at enabling users with no expertise in visualization and data analysis to get an overview of their corpus, and to interact with it: exploring, querying, filtering, panning, and zooming on regions of semantic interest. Three use cases are discussed to illustrate Cartolabe's versatility and ability to bring large-scale textual corpus visualization and exploration to a wide audience.

8.
IEEE Trans Vis Comput Graph ; 27(2): 1775-1785, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33095715

RESUMO

We propose a new approach-called PK-clustering-to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering approach and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 4) evaluates the consensus between user-selected algorithms and 5) allows users to review details and iteratively update the acquired knowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback from social scientists. We believe our clustering approach offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often randomly selected black-box clustering algorithms.

9.
IEEE Trans Vis Comput Graph ; 27(1): 1-13, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31398121

RESUMO

Parallel Aggregated Ordered Hypergraph(PAOH) is a novel technique to visualize dynamic hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several vertices. Hypergraphs can be used to model networks of business partners or co-authorship networks with multiple authors per article. A dynamic hypergraph evolves over discrete time slots. PAOH represents vertices as parallel horizontal bars and hyperedges as vertical lines, using dots to depict the connections to one or more vertices. We describe a prototype implementation of Parallel Aggregated Ordered Hypergraph, report on a usability study with 9 participants analyzing publication data, and summarize the improvements made. Two case studies and several examples are provided. We believe that PAOH is the first technique to provide a highly readable representation of dynamic hypergraphs. It is easy to learn and well suited for medium size dynamic hypergraphs (50-500 vertices) such as those commonly generated by digital humanities projects-our driving application domain.

10.
IEEE Comput Graph Appl ; 40(5): 108-119, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32833626

RESUMO

The American National Academies of Sciences, Engineering, and Medicine (NASEM) has recently released the report "Reproducibility and Replicability in Science." The report has prompted discussions within many disciplines about the extent of the current adoption of reproducibility and replicability, the challenges involved in publishing reproducible results as well as strategies for improving. We organized a panel at the IEEE VIS conference 2019 to start a discussion on the reproducibility challenges faced by the visualization community and how those challenges might be addressed. In this viewpoint, we summarize key findings of the NASEM report, the panel discussion, and outline a set of recommendations for the visualization community.

11.
IEEE Trans Vis Comput Graph ; 26(2): 1347-1360, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-30222575

RESUMO

We present PANENE, a progressive algorithm for approximate nearest neighbor indexing and querying. Although the use of k-nearest neighbor (KNN) libraries is common in many data analysis methods, most KNN algorithms can only be queried when the whole dataset has been indexed, i.e., they are not online. Even the few online implementations are not progressive in the sense that the time to index incoming data is not bounded and cannot satisfy the latency requirements of progressive systems. This long latency has significantly limited the use of many machine learning methods, such as t-SNE, in interactive visual analytics. PANENE is a novel algorithm for Progressive Approximate k-NEarest NEighbors, enabling fast KNN queries while continuously indexing new batches of data. Following the progressive computation paradigm, PANENE operations can be bounded in time, allowing analysts to access running results within an interactive latency. PANENE can also incrementally build and maintain a cache data structure, a KNN lookup table, to enable constant-time lookups for KNN queries. Finally, we present three progressive applications of PANENE, such as regression, density estimation, and responsive t-SNE, opening up new opportunities to use complex algorithms in interactive systems.

12.
Artigo em Inglês | MEDLINE | ID: mdl-30136987

RESUMO

Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We present multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography. We then introduce a declarative model-a simple yet expressive JSON grammar associated with visual semantics-that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.

13.
Artigo em Inglês | MEDLINE | ID: mdl-30136993

RESUMO

This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work.

14.
IEEE Comput Graph Appl ; 38(1): 91-108, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-28991735

RESUMO

Art historians have traditionally used physical light boxes to prepare exhibits or curate collections. On a light box, they can place slides or printed images, move the images around at will, group them as desired, and visual-ly compare them. The transition to digital images has rendered this workflow obsolete. Now, art historians lack well-designed, unified interactive software tools that effectively support the operations they perform with physi-cal light boxes. To address this problem, we designed ARIES (ARt Image Exploration Space), an interactive image manipulation system that enables the exploration and organization of fine digital art. The system allows images to be compared in multiple ways, offering dynamic overlays analogous to a physical light box, and sup-porting advanced image comparisons and feature-matching functions, available through computational image processing. We demonstrate the effectiveness of our system to support art historians tasks through real use cases.

15.
IEEE Trans Vis Comput Graph ; 23(8): 1977-1987, 2017 08.
Artigo em Inglês | MEDLINE | ID: mdl-28113667

RESUMO

The stated goal for visual data exploration is to operate at a rate that matches the pace of human data analysts, but the ever increasing amount of data has led to a fundamental problem: datasets are often too large to process within interactive time frames. Progressive analytics and visualizations have been proposed as potential solutions to this issue. By processing data incrementally in small chunks, progressive systems provide approximate query answers at interactive speeds that are then refined over time with increasing precision. We study how progressive visualizations affect users in exploratory settings in an experiment where we capture user behavior and knowledge discovery through interaction logs and think-aloud protocols. Our experiment includes three visualization conditions and different simulated dataset sizes. The visualization conditions are: (1) blocking, where results are displayed only after the entire dataset has been processed; (2) instantaneous, a hypothetical condition where results are shown almost immediately; and (3) progressive, where approximate results are displayed quickly and then refined over time. We analyze the data collected in our experiment and observe that users perform equally well with either instantaneous or progressive visualizations in key metrics, such as insight discovery rates and dataset coverage, while blocking visualizations have detrimental effects.

16.
IEEE Trans Vis Comput Graph ; 23(1): 31-40, 2017 01.
Artigo em Inglês | MEDLINE | ID: mdl-27514053

RESUMO

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.

17.
IEEE Trans Vis Comput Graph ; 22(1): 639-48, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26390473

RESUMO

In this article, we investigate methods for suggesting the interactivity of online visualizations embedded with text. We first assess the need for such methods by conducting three initial experiments on Amazon's Mechanical Turk. We then present a design space for Suggested Interactivity (i. e., visual cues used as perceived affordances-SI), based on a survey of 382 HTML5 and visualization websites. Finally, we assess the effectiveness of three SI cues we designed for suggesting the interactivity of bar charts embedded with text. Our results show that only one cue (SI3) was successful in inciting participants to interact with the visualizations, and we hypothesize this is because this particular cue provided feedforward.

18.
IEEE Trans Vis Comput Graph ; 20(12): 1963-72, 2014 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26356910

RESUMO

We describe a method for assessing the visualization literacy (VL) of a user. Assessing how well people understand visualizations has great value for research (e. g., to avoid confounds), for design (e. g., to best determine the capabilities of an audience), for teaching (e. g., to assess the level of new students), and for recruiting (e. g., to assess the level of interviewees). This paper proposes a method for assessing VL based on Item Response Theory. It describes the design and evaluation of two VL tests for line graphs, and presents the extension of the method to bar charts and scatterplots. Finally, it discusses the reimplementation of these tests for fast, effective, and scalable web-based use.


Assuntos
Gráficos por Computador , Alfabetização , Testes Psicológicos , Leitura , Análise e Desempenho de Tarefas , Compreensão , Humanos
19.
IEEE Trans Vis Comput Graph ; 20(12): 2082-91, 2014 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26356922

RESUMO

We present Bertifier, a web app for rapidly creating tabular visualizations from spreadsheets. Bertifier draws from Jacques Bertin's matrix analysis method, whose goal was to "simplify without destroying" by encoding cell values visually and grouping similar rows and columns. Although there were several attempts to bring this method to computers, no implementation exists today that is both exhaustive and accessible to a large audience. Bertifier remains faithful to Bertin's method while leveraging the power of today's interactive computers. Tables are formatted and manipulated through crossets, a new interaction technique for rapidly applying operations on rows and columns. We also introduce visual reordering, a semi-interactive reordering approach that lets users apply and tune automatic reordering algorithms in a WYSIWYG manner. Sessions with eight users from different backgrounds suggest that Bertifier has the potential to bring Bertin's method to a wider audience of both technical and non-technical users, and empower them with data analysis and communication tools that were so far only accessible to a handful of specialists.


Assuntos
Gráficos por Computador , Internet , Software , Apresentação de Dados , Humanos
20.
IEEE Trans Vis Comput Graph ; 20(12): 2291-300, 2014 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26356943

RESUMO

We present an exploration and a design space that characterize the usage and placement of word-scale visualizations within text documents. Word-scale visualizations are a more general version of sparklines--small, word-sized data graphics that allow meta-information to be visually presented in-line with document text. In accordance with Edward Tufte's definition, sparklines are traditionally placed directly before or after words in the text. We describe alternative placements that permit a wider range of word-scale graphics and more flexible integration with text layouts. These alternative placements include positioning visualizations between lines, within additional vertical and horizontal space in the document, and as interactive overlays on top of the text. Each strategy changes the dimensions of the space available to display the visualizations, as well as the degree to which the text must be adjusted or reflowed to accommodate them. We provide an illustrated design space of placement options for word-scale visualizations and identify six important variables that control the placement of the graphics and the level of disruption of the source text. We also contribute a quantitative analysis that highlights the effect of different placements on readability and text disruption. Finally, we use this analysis to propose guidelines to support the design and placement of word-scale visualizations.

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