For example, time continues forward serially, but weeks, months, and years are periods that recur. While there are extensive visualization techniques for exploring serial data, and a few for exploring periodic data, no existing technique simultaneously displays serial and periodic attributes of a data set.
We introduce a spiral visualization technique, which displays data along a spiral to highlight serial attributes along the spiral axis and periodic ones along the radii. While previous research has focused on searching and browsing, overview tasks are often overlooked.
We introduce a novel interactive visual overview of event sequences called LifeFlow. LifeFlow scales to any number of records, summarizes all possible sequences, and highlights the temporal spacing of the events within sequences. We conducted two case studies with healthcare and transportation domain experts to illustrate the usefulness of LifeFlow.
We also conducted a user study with ten participants which confirmed that after 15 minutes of training novice users were able to rapidly answer questions about the prevalence and temporal characteristics of sequences, find anomalies, and gain significant insight from the data.
Visualization has been successfully applied to analyse time-dependent data for a long time now. Lately, a number of new approaches have been introduced, promising more effective graphs especially for large datasets and multiparameter data.
In this paper, we give an overview on the visualization of t Abstract - Cited by 43 4 self - Add to MetaCart Visualization has been successfully applied to analyse time-dependent data for a long time now.
In this paper, we give an overview on the visualization of time-series data and the available techniques. We provide a taxonomy and discuss general aspects of time-dependent data. After an overview on conventional techniques we discuss techniques for analysing time-dependent multivariate data sets in more detail. After this, we give an overview on dynamic presentation techniques and event-based visualization.
Draper, Yarden Livnat, Richard F. Riesenfeld , Radial visualization, or the practice of displaying data in a circular or elliptical pattern, is an increasingly common technique in information visualization research.
In spite of its prevalence, little work has been done to study this visualization paradigm as a methodology in its own right. Abstract - Cited by 28 1 self - Add to MetaCart Radial visualization, or the practice of displaying data in a circular or elliptical pattern, is an increasingly common technique in information visualization research.
We provide a historical review of radial visualization, tracing it to its roots in centuries-old statistical graphics. We then identify the types of problem domains to which modern radial visualization techniques have been applied. A taxonomy for radial visualization is proposed in the form of seven design patterns encompassing nearly all recent works in this area.
From an analysis of these patterns, we distill a series of design considerations that system builders can use to create new visualizations that address aspects of the design space that have not yet been explored. It is hoped that our taxonomy will provide a framework for facilitating discourse among researchers and stimulate the development of additional theories and systems involving radial visualization as a distinct design metaphor. International Conference on Human-Computer Interaction , Visualizing time series data is useful to support discovery of relations and patterns in financial, genomic, medical and other applications.
In most time series, measurements are equally spaced over time. This paper discusses the challenges for unevenly-spaced time series data and presents This paper discusses the challenges for unevenly-spaced time series data and presents four methods to represent them: sampled events, aggregated sampled events, event index and interleaved event index. Test II shows MV outperforms, though mostly insignificantly, all the other methods on the total time taken to complete all the tasks.
Test III shows MV slightly outperformed by all methods except for Vertical at the task of identifying relationships between variables. I think this is because the users were able to glean extra information the variable relationships while trying to identify individual variable periods in the methods where the space was shared — in fact, many times the subjects identified the variable relationships immediately. With MV, the users gained no extra information from identifying the periods, and looking for relationships was a whole new task to them.
The Vertical method tended to introduce a lot of confusion, because the vertical splitting of the rectangles inadvertently introduced a lot of vertical patterns that made vertical patterns due to periodicity harder to find. It also made variables appear to be inversely related, as the different colored vertical lines appeared side by side See Figure Test IV, though it did not produce statistically significant results, seems to indicate that the Horizontal and MV methods suffered most from the clutter introduced by adding a third variable.
For MV, this is consistent with the results from Test I — that MV is more affected by the reduction of available space for each variable than other methods are by being forced to fill the shared space with more variables.
For Horizontal, I think this is due to the fact that adding more horizontal lines per rectangle increases the vertical separation between values of the same variable, making vertical patterns harder to spot. The subjects seemed to be excited about using the visualization tool, and largely enjoyed the process of completing the tasks, in particular when they were able to quickly spot patterns in the data they were working with.
The Vertical method seemed to cause a lot of confusion, and the study results bear that out somewhat. However, despite that, the Color Blend method did quite well. So, which of the methods is better?
What are any of these methods good for? Obviously, the data under consideration needs to either be known to be periodic, or needs to be evaluated for periodicity. If only 2 variables need to be displayed, then Multiple Views is probably the best choice. The Horizontal method is a close second. Its effectiveness for that many variables would need to be explored more, but it shows some promise. Interactive Visualization of Serial Periodic Data.
Figure 1: Light information for January , displayed with a hour period. We see a very slanted diagonal pattern. Figure 2: Light information for January , displayed with a hour period. The diagonal pattern is a little less slanted.
Figure 3: Light information for January , displayed with a hour period. The diagonal pattern becomes less and less slanted as we get closer to the period of the variable. Figure 4: Light information for January , displayed with a hour period. The vertical pattern we see indicates that we have found the period of the variable which, in this case, was obviously 24 to begin with.
Figure 5: Diagonal method, 2 variables. A vertical pattern is about to emerge for both red and green, both of which have the same period. Figure 6: Horizontal method, 2 variables. A vertical pattern has emerged for both red and green, which are inversely related. Note that there is some vertical discontinuity for both colors. This becomes worse in the 3 variable case. Figure 7: Vertical method, 3 variables.
A vertical pattern is about to emerge for both red and green, which share the same period. Note that although blue is non-periodic, we can see definite vertical strips of it.
Figure 8: Color Blend method, 3 variables. We see red and green have the same period which is currently displayed , and are inverses. Figure 9: Multiple Views method, 3 variables. We are at the correct period for red.
Green and blue are non-periodic — note the telltale absence of diagonal lines in either. Figure Vertical method, 3 variables. We are not at the correct period for any variable, but we see strong vertical patterns.
Also, the variables falsely appear to alternate, creating the impression that there is some inverse relationship. The Z value is the confidence interval.
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