The Attraction Effect in Information Visualization

International audience—The attraction effect is a well-studied cognitive bias in decision making research, where one's choice between two alternatives is influenced by the presence of an irrelevant (dominated) third alternative. We examine whether this cognitive bias, so far only tested with three alternatives and simple presentation formats such as numerical tables, text and pictures, also appears in visualiza-tions. Since visualizations can be used to support decision making — e.g., when choosing a house to buy or an employee to hire — a systematic bias could have important implications. In a first crowdsource experiment, we indeed partially replicated the attraction effect with three alternatives presented as a numerical table, and observed similar effects when they were presented as a scatterplot. In a second experiment, we investigated if the effect extends to larger sets of alternatives, where the number of alternatives is too large for numerical tables to be practical. Our findings indicate that the bias persists for larger sets of alternatives presented as scatterplots. We discuss implications for future research on how to further study and possibly alleviate the attraction effect

Accounting for Availability Biases in Information Visualization

International audienceThe availability heuristic is a strategy that people use to make quick decisions but often lead to systematic errors. We propose three ways that visualization could facilitate unbiased decision-making. First, visualizations can alter the way our memory stores the events for later recall, so as to improve users' long-term intuitions. Second, the known biases could lead to new visualization guidelines. Third, we suggest the design of decision-making tools that are inspired by heuristics, e.g. suggesting intuitive approximations, rather than target to present exhaustive comparisons of all possible outcomes, or automated solutions for choosing decisions

ProS: Data Series Progressive k-NN Similarity Search and Classification with Probabilistic Quality Guarantees

Existing systems dealing with the increasing volume of data series cannot guarantee interactive response times, even for fundamental tasks such as similarity search. Therefore, it is necessary to develop analytic approaches that support exploration and decision making by providing progressive results, before the final and exact ones have been computed. Prior works lack both efficiency and accuracy when applied to large-scale data series collections. We present and experimentally evaluate ProS, a new probabilistic learning-based method that provides quality guarantees for progressive Nearest Neighbor (NN) query answering. We develop our method for k-NN queries and demonstrate how it can be applied with the two most popular distance measures, namely, Euclidean and Dynamic Time Warping (DTW). We provide both initial and progressive estimates of the final answer that are getting better during the similarity search, as well suitable stopping criteria for the progressive queries. Moreover, we describe how this method can be used in order to develop a progressive algorithm for data series classification (based on a k-NN classifier), and we additionally propose a method designed specifically for the classification task. Experiments with several and diverse synthetic and real datasets demonstrate that our prediction methods constitute the first practical solutions to the problem, significantly outperforming competing approaches. This paper was published in the VLDB Journal (2022)

Trade-offs Between a Vertical Shared Display and Two Desktops in a Collaborative Path-Finding Task

International audienceLarge vertical displays are considered well adapted for collaboration, due to their display surface and the space in front of them that can accommodate multiple people. However, there are few studies that empirically support this assertion, and they do not quantitatively assess the differences of collaboration in front of a shared display compared to a non-shared setup, such as multiple desktops with a common view. In this paper, we compare a large shared vertical display with two desktops, when pairs of users learn to perform a path-planning task. Our results did not indicate a significant difference in learning between the two setups, but found that participants adopted different task strategies. Moreover, while pairs were overall faster with the two desktops, quality was more consistent in the vertical shared display where pairs spent more time communicating, even though there is a-priori more implicit collaboration in this setu

A Mixed Approach for the Evaluation of a Guided Exploratory Visualization System

HAL Id : hal-01153887, version 1 HAL Id : hal-01153887, version 1International audienceWe summarise and reflect upon our experience in evaluating a guided exploratory visualization system. Our system guides users in their exploration of multidimensional datasets to pertinent views of their data, where the notion of pertinence is defined by automatic indicators, such as the amount of visual patterns in the view, and subjective user feedback obtained during their interaction with the tool. To evaluate this type of system, we argue for deploying a collection of validation methods that are: user-centered, observing the utility and effectiveness of the system for the end-user; and algorithm-centered, analysing the computational behaviour of the system. We report on observations and lessons learnt from working with expert users both for the design and the evaluation of our system

Geo-Storylines: Integrating Maps into Storyline Visualizations

International audienceStoryline visualizations are a powerful way to compactly visualize how the relationships between people evolve over time. Real-world relationships often also involve space, for example the cities that two political rivals visited together or alone over the years. By default, Storyline visualizations only show implicitly geospatial co-occurrence between people (drawn as lines), by bringing their lines together. Even the few designs that do explicitly show geographic locations only do so in abstract ways (e.g., annotations) and do not communicate geospatial information, such as the direction or extent of their political campains. We introduce Geo-Storylines, a collection of visualisation designs that integrate geospatial context into Storyline visualizations, using different strategies for compositing time and space. Our contribution is twofold. First, we present the results of a sketching workshop with 11 participants, that we used to derive a design space for integrating maps into Storylines. Second, by analyzing the strengths and weaknesses of the potential designs of the design space in terms of legibility and ability to scale to multiple relationships, we extract the three most promising: Time Glyphs, Coordinated Views, and Map Glyphs. We compare these three techniques first in a controlled study with 18 participants, under five different geospatial tasks and two maps of different complexity. We additionally collected informal feedback about their usefulness from domain experts in data journalism. Our results indicate that, as expected, detailed performance depends on the task. Nevertheless, Coordinated Views remain a highly effective and preferred technique across the board

Visualisation localisée en mouvement pour la natation

International audienceCompetitive sports coverage increasingly includes information on athlete or team statistics and records. Sports video coverage has traditionally embedded representations of this data in fixed locations on the screen, but more recently also attached representations to athletes or other targets in motion. These publicly used representations so far have been rather simple and systematic investigations of the research space of embedded visualizations in motion are still missing. Here we report on our preliminary research in the domain of professional and amateur swimming. We analyzed how visualizations are currently added to the coverage of Olympics swimming competitions and then plan to derive a design space for embedded data representations for swimming competitions. We are currently conducting a crowdsourced survey to explore which kind of swimming-related data general audiences are interested in, in order to identify opportunities for additional visualizations to be added to swimming competition coverage

Perceptual Affordances of Wall-Sized Displays for Visualization Applications: Color

International audienceWall-sized displays offer the opportunity to display very large information spaces. Most data representations can be scaled to wall size but display walls are not simply big desktop monitors. We do not yet know how the perceptual affordances of a wall, such as the wide viewing angles they cover, affect how data is perceived and comprehended. In this paper we call for more studies on the perception of data on wall-sized displays and discuss-with the example of color-several aspects of wall setups that we hypothesize will most affect the perception of this visual variable

Progressive Similarity Search on Time Series Data

International audienceTime series data are increasing at a dramatic rate, yet their analysis remains highly relevant in a wide range of human activities. Due to their volume, existing systems dealing with time series data cannot guarantee interactive response times, even for fundamental tasks such as similarity search. Therefore , in this paper, we present our vision to develop analytic approaches that support exploration and decision making by providing progressive results, before the final and exact ones have been computed. We demonstrate through experiments that providing first approximate and then progressive answers is useful (and necessary) for similarity search queries on very large time series data. Our findings indicate that there is a gap between the time the most similar answer is found and the time when the search algorithm terminates, resulting in inflated waiting times without any improvement. We present preliminary ideas on computing probabilistic estimates of the final results that could help users decide when to stop the search process, i.e., deciding when improvement in the final answer is unlikely, thus eliminating waiting time. Finally, we discuss two additional challenges: how to compute efficiently these probabilistic estimates, and how to communicate them to users