Luana Micallef

eulerAPE: Drawing Area-Proportional 3-Venn Diagrams Using Ellipses

Venn diagrams with three curves are used extensively in various medical and scientific disciplines to visualize relationships between data sets and facilitate data analysis. The area of the regions formed by the overlapping curves is often directly proportional to the cardinality of the depicted set relation or any other related quantitative data. Drawing these diagrams manually is difficult and current automatic drawing methods do not always produce appropriate diagrams. Most methods depict the data sets as circles, as they perceptually pop out as complete distinct objects due to their smoothness and regularity. However, circles cannot draw accurate diagrams for most 3-set data and so the generated diagrams often have misleading region areas. Other methods use polygons to draw accurate diagrams. However, polygons are non-smooth and non-symmetric, so the curves are not easily distinguishable and the diagrams are difficult to comprehend. Ellipses are more flexible than circles and are similarly smooth, but none of the current automatic drawing methods use ellipses. We present eulerAPE as the first method and software that uses ellipses for automatically drawing accurate area-proportional Venn diagrams for 3-set data. We describe the drawing method adopted by eulerAPE and we discuss our evaluation of the effectiveness of eulerAPE and ellipses for drawing random 3-set data. We compare eulerAPE and various other methods that are currently available and we discuss differences between their generated diagrams in terms of accuracy and ease of understanding for real world data.

Visualizing Sets and Set-typed Data: State-of-the-Art and Future Challenges

A variety of data analysis problems can be modelled by defining multiple sets over a collection of elements and analyzing the relations between these sets. Despite their simple concept, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into 7 main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address with these challenges.

The State-of-the-Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non‐trivial problem due to the large number of possible relations between them. We provide a systematic overview of state‐of‐the‐art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net.

Towards Perceptual Optimization of the Visual Design of Scatterplots

Designing a good scatterplot can be difficult for non-experts in visualization, because they need to decide on many parameters, such as marker size and opacity, aspect ratio, color, and rendering order. This paper contributes to research exploring the use of perceptual models and quality metrics to set such parameters automatically for enhanced visual quality of a scatterplot. A key consideration in this paper is the construction of a cost function to capture several relevant aspects of the human visual system, examining a scatterplot design for some data analysis task. We show how the cost function can be used in an optimizer to search for the optimal visual design for a user’s dataset and task objectives (e.g., “reliable linear correlation estimation is more important than class separation”). The approach is extensible to different analysis tasks. To test its performance in a realistic setting, we pre-calibrated it for correlation estimation, class separation, and outlier detection. The optimizer was able to produce designs that achieved a level of speed and success comparable to that of those using human-designed presets (e.g., in R or MATLAB). Case studies demonstrate that the approach can adapt a design to the data, to reveal patterns without user intervention.

EulerForce: Force-directed layout for Euler diagrams

Euler diagrams use closed curves to represent sets and their relationships. They facilitate set analysis, as humans tend to perceive distinct regions when closed curves are drawn on a plane. However, current automatic methods often produce diagrams with irregular, non-smooth curves that are not easily distinguishable. Other methods restrict the shape of the curve to for instance a circle, but such methods cannot draw an Euler diagram with exactly the required curve intersections for any set relations. In this paper, we present eulerForce, as the first method to adopt a force-directed approach to improve the layout and the curves of Euler diagrams generated by current methods. The layouts are improved in quick time. Our evaluation of eulerForce indicates the benefits of a force-directed approach to generate comprehensible Euler diagrams for any set relations in relatively fast time.

Crowdsourcing for Information Visualization: Promises and Pitfalls

Crowdsourcing offers great potential to overcome the limitations of controlled lab studies. To guide future designs of crowdsourcing-based studies for visualization, we review visualization research that has attempted to leverage crowdsourcing for empirical evaluations of visualizations. We discuss six core aspects for successful employment of crowdsourcing in empirical studies for visualization – participants, study design, study procedure, data, tasks, and metrics & measures. We then present four case studies, discussing potential mechanisms to overcome common pitfalls. This chapter will help the visualization community understand how to effectively and efficiently take advantage of the exciting potential crowdsourcing has to offer to support empirical visualization research.

MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection

BackgroundDispersed biomedical databases limit user exploration to generate structured knowledge. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. In addition, potential errors in the data are difficult to detect in their free formats. Devising a visualization that synthesizes multiple sources in such a way that links between data sources are transparent, and uncertainties, such as data conflicts, are salient is challenging.ResultsTo investigate the requirements and challenges of uncertainty-aware visualizations of linked data, we developed MediSyn, a system that synthesizes medical datasets to support drug treatment selection. It uses a matrix-based layout to visually link drugs, targets (e.g., mutations), and tumor types. Data uncertainties are salient in MediSyn; for example, (i) missing data are exposed in the matrix view of drug-target relations; (ii) inconsistencies between datasets are shown via overlaid layers; and (iii) data credibility is conveyed through links to data provenance.ConclusionsThrough the synthesis of two manually curated datasets, cancer treatment biomarkers and drug-target bioactivities, a use case shows how MediSyn effectively supports the discovery of drug-repurposing opportunities. A study with six domain experts indicated that MediSyn benefited the drug selection and data inconsistency discovery. Though linked publication sources supported user exploration for further information, the causes of inconsistencies were not easy to find. Additionally, MediSyn could embrace more patient data to increase its informativeness. We derive design implications from the findings.

Augmenting objects at home through programmable sensor tokens: A design journey

Abstract End-user development for the home has been gaining momentum in research. Previous works demonstrate feasibility and potential but there is a lack of analysis of the extent of technology needed and its impact on the diversity of activities that can be supported. We present a design exploration with a tangible end-user toolkit for programming smart tokens embedding different sensing technologies. Our system allows to augment physical objects with smart tags and use trigger-action programming with multiple triggers to define smart behaviors. We contribute through a field-oriented study that provided insights on (i) households activities as emerging from peoples lived experience in terms of high-level goals, their ephemerality or recurrence, and the types of triggers, actions and interactions with augmented objects, and (ii) the programmability needed for supporting desired behaviors. We conclude that, while trigger–action covers most scenarios, more advanced programming and direct interaction with physical objects spur novel uses.

mailVis: Visualizing emailbox for re-finding emails

We present the design and implementation of mailVis, an interactive visual interface for email boxes that facilitates re-finding of emails. Email re-finding tasks can be challenging, involving scanning of many emails and modifying the query as the search progresses. We designed mailVis for such tasks in which the user would benefit from having memory clues and multiple options to direct the search. During the design process, we devised a novel interaction technique, filter swipe, that combines filtering and selection into one action for rapidly skimming individual items in a data set.

Special Issue: Guest Editors' Introduction

n Corresponding author. including intersection, containment, and disjointness. These diagrams have become the foundations of various visual languages and have notably facilitated the modelling of, and logical reasoning about, complex systems. Over the years, they have been extensively used in areas such as biosciences, business, criminology and national security to intuitively visualize relationships and relative cardinalities of sets. This widespread adoption has allowed analysis of complex collections of data. This Special Issue contains extended versions of the papers presented at the third International Workshop on Euler Diagrams (Euler Diagrams 2012), held as a workshop of the seventh International Conference on the Theory and Application of Diagrams (Diagrams 2012) in Canterbury, UK. In addition to the call for papers from the workshop, an open call for papers was made. The workshop covered all aspects of Euler diagram research, particularly areas such as: drawability and readability; layouts and diagram generation; logic and reasoning; information visualization; aesthetics; and evaluation, including comparison to other representations. This workshop was the third in the Euler Diagrams series (after two successful workshops in 2004 and 2005) and the first to run in conjunction with another conference. The first paper of this Special Issue is an invited survey paper by Peter Rodgers. It covers all main aspects of Euler diagrams from foundational issues, through generation and drawing problems, to applications and various extensions to the basic notation. The survey will serve as both a comprehensive introduction for those new to the subject, and a useful overview of the state-of-the-art for established researchers in the area. Each section also includes a brief account of key open questions. The next paper, by Koji Mineshima, Yuri Sato, Ryo Takemura and Mitsuhiro Okada, entitled Towards Explaining the Cognitive Efficacy of Euler Diagrams in Syllogistic Reasoning, examines an application for which Euler diagrams are particularly suited: syllogisms. It is known that Euler diagrams, when compared with Venn diagrams or textual information, are more effective for reasoning about syllogisms. By bringing together a number of different perspectives, the authors begin to form a model about where the efficacy comes from.