Continuum

Abstract

Temporal events, while often discrete, also have interesting relationships within and across times: larger events are often collections of smaller more discrete events (battles within wars; artists' works within a form); events at one point also have correlations with events at other points (a play written in one period is related to its performance over a period of time). Most temporal visualisations, however, only represent discrete data points or single data types along a single timeline: this event started here and ended there; this work was published at this time; this tag was popular for this period. In order to represent richer, faceted attributes of temporal events, we present Continuum. Con-

Table of contents

“…This metric calculates the absolute difference between the user's judgement and the true magnitude difference. The log 2 parameter was found appropriate for error judgments, while the 1 8 parameter prevents a distortion of the results towards the lower end of the error scale, since the absolute error was sometimes close to 0. Trials were marked as outliers when error was beyond two standard deviations from the mean for a given task, chart, location, breakpoint, and answer percent (4% of all trials), and were removed from further analysis.…”

“…7 shows an example of a cut-out chart. This type of chart is frequently used to display data along timelines [1]. The top part of the chart shows the whole dataset and highlights the focus while the bottom shows the focus extended to the width of the display area.…”

Evaluating the Effect of Timeline Shape on Visualization Task Performance

“…In addition, Aigner et al [1] include a review of several timeline visualizations with varying shapes, data arrangements, and encodings. These include examples of linear [2,27], circular [13,20], and spiral [11,36] timeline visualizations. In each of these cases the timeline shape was chosen to best emphasize certain features of the data or to assist specific visualization tasks.…”

“…In a review of related work and discussion with experts working with such data in the fields of history and personal health informatics, we identified 3 types of temporal event sequence data: (1) A non-recurring series of events, e.g., world history where events do not repeat. (2) A recurring sequence of events where the events happen at specific intervals, e.g., a company's quarterly financial reports. (3) A mixture of recurring and non-recurring sequences.…”

Designing Group Annotations and Process Visualizations for Role-Based Collaboration

“…We are exploring three design ideas: (1) A geo-spatial overview of the contributions that filters out details and displays what information was contributed 'where' on the map and 'by what expert' (e.g., a similar design concept is used for example in the Google Maps Mashups generated for Hurricane Katrina). (2) A temporal overview of the process: a n-timeline view (one timeline for each of the 'n' experts or roles) displaying 'who' did what 'type of operation' (add, change, delete), 'when', and 'temporal patterns' among the contributions, time spent in each task phase, and deadlines (e.g., see visualization tools in Ganoe et al [11] and Andre' et al [1]); (3) A conceptual view of the decision space: elements of risks and judgments contributed by each expert are grouped by the decision alternatives (or by content categories, or tags, defined by the team). The resulting breakdown of judgments is displayed either in a bar chart with alternative solutions (or content categories) defining the number of bars or a tabular representation with solutions as columns and pro or against judgments as rows (see the CACHE system in Billman et al [4]).…”