Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v = m * 10e . We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyse error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.Comment: Preprint and Author Version of a Full Paper, accepted to the 2023 IEEE Visualization Conference (VIS

Segundo FOLGADO FLÓREZ, Teoría eclesial en el Pastor de Hermas, Real Monasterio de El Escorial (Biblioteca «La Ciudad de Dios». I. Libros, n. 30), 1979, 142 pp., 17 X 24. [RECENSIÓN]

Visual comparison of directed acyclic graphs (DAGs) is commonly encountered in various disciplines (e.g., finance, biology). Still, knowledge about humans' perception of their similarity is currently quite limited. By similarity perception, we mean how humans perceive commonalities and differences of DAGs and herewith come to a similarity judgment. To fill this gap, we strive to identify factors influencing the DAG similarity perception. Therefore, we conducted a card sorting study employing a quantitative and qualitative analysis approach to identify (1) groups of DAGs the participants perceived as similar and (2) the reasons behind their groupings. We also did an extended analysis of our collected data to (1) reveal specifics of the influencing factors and (2) investigate which strategies are employed to come to a similarity judgment. Our results suggest that DAG similarity perception is mainly influenced by the number of levels, the number of nodes on a level, and the overall shape of the DAG. We also identified three strategies used by the participants to form groups of similar DAGs: divide and conquer, respecting the entire dataset and considering the factors one after the other, and considering a single factor. Factor specifics are, e.g., that humans on average consider four factors while judging the similarity of DAGs. Building an understanding of these processes may inform the design of comparative visualizations and strategies for interacting with them. The interaction strategies must allow the user to apply her similarity judgment strategy to the data. The considered factors bear information on, e.g., which factors are overlooked by humans and thus need to be highlighted by the visualization

Network of the Day: Aggregating and Visualizing Entity Networks from Online Sources

This software demonstration paper presents a project on the interactive visualization of social media data. The data presentation fuses German Twitter data and a social relation network extracted from German online news. Such fusion allows for comparative analysis of the two types of media. Our system will additionally enable users to explore relationships between named entities, and to investigate events as they develop over time. Cooperative tagging of relationships is enabled through the active involvement of users. The system is available online for a broad user audience

Visual Validation versus Visual Estimation: A Study on the Average Value in Scatterplots

We investigate the ability of individuals to visually validate statistical models in terms of their fit to the data. While visual model estimation has been studied extensively, visual model validation remains under-investigated. It is unknown how well people are able to visually validate models, and how their performance compares to visual and computational estimation. As a starting point, we conducted a study across two populations (crowdsourced and volunteers). Participants had to both visually estimate (i.e, draw) and visually validate (i.e., accept or reject) the frequently studied model of averages. Across both populations, the level of accuracy of the models that were considered valid was lower than the accuracy of the estimated models. We find that participants' validation and estimation were unbiased. Moreover, their natural critical point between accepting and rejecting a given mean value is close to the boundary of its 95% confidence interval, indicating that the visually perceived confidence interval corresponds to a common statistical standard. Our work contributes to the understanding of visual model validation and opens new research opportunities.Comment: Preprint and Author Version of a Short Paper, accepted to the 2023 IEEE Visualization Conference (VIS

New insights into the suitability of the third dimension for visualizing multivariate/multidimensional data: a study based on loss of quality quantification

Most visualization techniques have traditionally used two-dimensional, instead of three-dimensional representations to visualize multidimensional and multivariate data. In this article, a way to demonstrate the underlying superiority of three-dimensional, with respect to two-dimensional, representation is proposed. Specifically, it is based on the inevitable quality degradation produced when reducing the data dimensionality. The problem is tackled from two different approaches: a visual and an analytical approach. First, a set of statistical tests (point classification, distance perception, and outlier identification) using the two-dimensional and three-dimensional visualization are carried out on a group of 40 users. The results indicate that there is an improvement in the accuracy introduced by the inclusion of a third dimension; however, these results do not allow to obtain definitive conclusions on the superiority of three-dimensional representation. Therefore, in order to draw further conclusions, a deeper study based on an analytical approach is proposed. The aim is to quantify the real loss of quality produced when the data are visualized in two-dimensional and three-dimensional spaces, in relation to the original data dimensionality, to analyze the difference between them. To achieve this, a recently proposed methodology is used. The results obtained by the analytical approach reported that the loss of quality reaches significantly high values only when switching from three-dimensional to two-dimensional representation. The considerable quality degradation suffered in the two-dimensional visualization strongly suggests the suitability of the third dimension to visualize data

An investigation of phishing awareness and education over time: When and how to best remind users

Security awareness and education programmes are rolled out in more and more organisations. However, their effectiveness over time and, correspondingly, appropriate intervals to remind users’ awareness and knowledge are an open question. In an attempt to address this open question, we present a field investigation in a German organisation from the public administration sector. With overall 409 employees, we evaluated (a) the effectiveness of their newly deployed security awareness and education programme in the phishing context over time and (b) the effectiveness of four different reminder measures – administered after the initial effect had worn off to a degree that no significant improvement to before its deployment was detected anymore. We find a significantly improved performance of correctly identifying phishing and legitimate emails directly after and four months after the programme’s deployment. This was not the case anymore after six months, indicating that reminding users after half a year is recommended. The investigation of the reminder measures indicates that measures based on videos and interactive examples perform best, lasting for at least another six months

An investigation of phishing awareness and education over time: When and how to best remind users

Security awareness and education programmes are rolled out in more and more organisations. However, their effectiveness over time and, correspondingly, appropriate intervals to remind users’ awareness and knowledge are an open question. In an attempt to address this open question, we present a field investigation in a German organisation from the public administration sector. With overall 409 employees, we evaluated (a) the effectiveness of their newly deployed security awareness and education programme in the phishing context over time and (b) the effectiveness of four different reminder measures – administered after the initial effect had worn off to a degree that no significant improvement to before its deployment was detected anymore. We find a significantly improved performance of correctly identifying phishing and legitimate emails directly after and four months after the programme’s deployment. This was not the case anymore after six months, indicating that reminding users after half a year is recommended. The investigation of the reminder measures indicates that measures based on videos and interactive examples perform best, lasting for at least another six months

Human-Centric Chronographics:Making Historical Time Memorable

A series of experiments is described, evaluating user recall of visualisations of historical chronology. Such visualisations are widely created but have not hitherto been evaluated. Users were tested on their ability to learn a sequence of historical events presented in a virtual environment (VE) fly-through visualisation, compared with the learning of equivalent material in other formats that are sequential but lack the 3D spatial aspect. Memorability is a particularly important function of visualisation in education. The measures used during evaluation are enumerated and discussed. The majority of the experiments reported compared three conditions, one using a virtual environment visualisation with a significant spatial element, one using a serial on-screen presentation in PowerPoint, and one using serial presentation on paper. Some aspects were trialled with groups having contrasting prior experience of computers, in the UK and Ukraine. Evidence suggests that a more complex environment including animations and sounds or music, intended to engage users and reinforce memorability, were in fact distracting. Findings are reported in relation to the age of the participants, suggesting that children at 11–14 years benefit less from, or are even disadvantaged by, VE visualisations when compared with 7–9 year olds or undergraduates. Finally, results suggest that VE visualisations offering a ‘landscape’ of information are more memorable than those based on a linear model. Keywords: timeline, chronographic

Interaction in the Visualization of Multivariate Networks

International audienceInteraction is a vital component in the visualization of multivariate networks. By allowing people to browse data sets with interactions like panning and zoom- ing, it enables much more information to be seen and explored than would oth- erwise be possible with static visualization. Overview-based interactions afford the user the ability to understand a complete picture of the data or informa- tion landscape and to decide where to direct her attention. Through search and filtering, interaction can reduce cognitive effort on users by allowing them to locate, focus on and understand subsets of the data in isolation. Pivoting and other navigational interactions at both the view- and data-level allow people to identify and then to transition between areas of interest. While there are methods for interacting with graphs and dimensions sep- arately, the combination of both needs special attention. The challenge is to clearly visualize multiple sets of individual dimensions as well as to offer a useful visual overview of data, and allow transitions between these to be easily under- stood. Moreover, we need to find ways to support users in navigating through the complex data space (graphs x dimensions) without "getting lost" without an overburden of interaction actions, as this might me frustrating for the user