Matthew D. Cooper

Revealing structure within clustered parallel coordinates displays

In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent tool for visualizing this type of data but has its limitations. This paper deals with one of its main limitations - how to visualize a large number of data items without hiding the inherent structure they constitute. We solve this problem by constructing clusters and using high precision textures to represent them. We also use transfer functions that operate on the high precision textures in order to highlight different aspects of the cluster characteristics. Providing predefined transfer functions as well as the support to draw customized transfer functions makes it possible to extract different aspects of the data. We also show how feature animation can be used as guidance when simultaneously analysing several clusters. This technique makes it possible to visually represent statistical information about clusters and thus guides the user, making the analysis process more efficient.

ActiviTree: Interactive Visual Exploration of Sequences in Event-Based Data Using Graph Similarity

The identification of significant sequences in large and complex event-based temporal data is a challenging problem with applications in many areas of todays information intensive society. Pure visual representations can be used for the analysis, but are constrained to small data sets. Algorithmic search mechanisms used for larger data sets become expensive as the data size increases and typically focus on frequency of occurrence to reduce the computational complexity, often overlooking important infrequent sequences and outliers. In this paper we introduce an interactive visual data mining approach based on an adaptation of techniques developed for Web searching, combined with an intuitive visual interface, to facilitate user-centred exploration of the data and identification of sequences significant to that user. The search algorithm used in the exploration executes in negligible time, even for large data, and so no pre-processing of the selected data is required, making this a completely interactive experience for the user. Our particular application area is social science diary data but the technique is applicable across many other disciplines.

Revealing structure in visualizations of dense 2D and 3D parallel coordinates

Parallel coordinates is a well-known technique used for visualization of multivariate data. When the size of the data sets increases the parallel coordinates display results in an image far too cluttered to perceive any structure. We tackle this problem by constructing high-precision textures to represent the data. By using transfer functions that operate on the high-precision textures, it is possible to highlight different aspects of the entire data set or clusters of the data. Our methods are implemented in both standard 2D parallel coordinates and 3D multi-relational parallel coordinates. Furthermore, when visualizing a larger number of clusters, a technique called ‘feature animation’ may be used as guidance by presenting various cluster statistics. A case study is also performed to illustrate the analysis process when analysing large multivariate data sets using our proposed techniques.

Parameter estimation variance of the single point active alignment method in optical see-through head mounted display calibration

The parameter estimation variance of the Single Point Active Alignment Method (SPAAM) is studied through an experiment where 11 subjects are instructed to create alignments using an Optical See-Through Head Mounted Display (OSTHMD) such that three separate correspondence point distributions are acquired. Modeling the OSTHMD and the subjects dominant eye as a pinhole camera, findings show that a correspondence point distribution well distributed along the users line of sight yields less variant parameter estimates. The estimated eye point location is studied in particular detail. The findings of the experiment are complemented with simulated data which show that image plane orientation is sensitive to the number of correspondence points. The simulated data also illustrates some interesting properties on the numerical stability of the calibration problem as a function of alignment noise, number of correspondence points, and correspondence point distribution.

Enabling design and interactive selection of haptic modes

The ever increasing size and complexity of volumetric data in a wide range of disciplines makes it useful to augment volume visualization tools with alternative modalities. Studies have shown that introducing haptics can significantly increase both exploration speed and precision. It is also capable of conveying material properties of data and thus has great potential to improve user performance in volume data exploration. In this paper we describe how recent advances in volume haptics can be used to build haptic modes—building blocks for haptic schemes. These modes have been used as base components of a toolkit allowing for more efficient development of haptic prototypes and applications. This toolkit allows interactive construction, configuration and fine-tuning of both visual and haptic representations of the data. The technology is also used in a pilot study to determine the most important issues and aspects in haptic volume data interaction and exploration, and how the use of haptic modes can facilitate the implementation of effective haptic schemes.

Depth cues and density in temporal parallel coordinates

This paper introduces Temporal Density Parallel Coordinates (TDPC) and Depth Cue Parallel Coordinates (DCPC) which extend the standard 2D parallel coordinates technique to capture time-varying dynamics. The proposed techniques can be used to analyse temporal positions of data items as well as temporal positions of changes occurring using 2D displays. To represent temporal changes, polygons (instead of traditional lines) are rendered in parallel coordinates. The results presented show that rendering polygons is superior at revealing large temporal changes. Both TDPC and DCPC have been efficiently implemented on the GPU allowing the visualization of thousands of data items over thousands of time steps at interactive frame rates.

Everyday Life Discoveries: Mining and Visualizing Activity Patterns in Social Science Diary Data

The ability to identify and examine patterns of activities is a key tool for social and behavioural science. In the past this has been done by statistical or purely visual methods but automated sequential pattern analysis through sophisticated data mining and visualization tools for pattern location and evaluation can open up new possibilities for interactive exploration of the data. This paper describes the addition of a sequential pattern identification method to the visual activity-analysis tool, VISUAL-TimePAcTS, and its effectiveness in the process of pattern analysis in social science diary data. The results have shown that the method correctly identifies patterns and conveys them effectively to the social scientist in a manner that allows them quick and easy understanding of the significance of the patterns.

A screen space quality method for data abstraction

The rendering of large data sets can result in cluttered displays and non‐interactive update rates, leading to time consuming analyses. A straightforward solution is to reduce the number of items, thereby producing an abstraction of the data set. For the visual analysis to remain accurate, the graphical representation of the abstraction must preserve the significant features present in the original data. This paper presents a screen space quality method, based on distance transforms, that measures the visual quality of a data abstraction. This screen space measure is shown to better capture significant visual structures in data, compared with data space measures. The presented method is implemented on the GPU, allowing interactive creation of high quality graphical representations of multivariate data sets containing tens of thousands of items.

Haptic Rendering of Dynamic Volumetric Data

With current methods for volume haptics in scientific visualization, features in time-varying data can freely move straight through the haptic probe without generating any haptic feedback-the algorithms are simply not designed to handle variation with time but consider only the instantaneous configuration when the haptic feedback is calculated. This article introduces haptic rendering of dynamic volumetric data to provide a means for haptic exploration of dynamic behavior in volumetric data. We show how haptic feedback can be produced that is consistent with volumetric data moving within the virtual environment and with data that, in itself, evolves over time. Haptic interaction with time-varying data is demonstrated by allowing palpation of a computerized tomography sequence of a beating human heart.

Visual clutter management in augmented reality: Effects of three label separation methods on spatial judgments

This paper reports an experiment comparing three label separation methods for reducing visual clutter in Augmented Reality (AR) displays. We contrasted two common methods of avoiding visual overlap by moving labels in the 2D view plane with a third that distributes overlapping labels in stereoscopic depth. The experiment measured user identification performance during spatial judgment tasks in static scenes. The threemethods were compared with a control condition in which no label separation method was employed. The results showed significant performance improvements, generally 15–30%, for all three methods over the control; however, these methods were statistically indistinguishable from each other. Indepth analysis showed significant performance degradation when the 2D view plane methods produced potentially confusing spatial correlations between labels and the markers they designate. Stereoscopically separated labels were subjectively judged harder to read than view-plane separated labels. Since measured performance was affected both by label legibility and spatial correlation of labels and their designated objects, it is likely that the improved spatial correlation of stereoscopically separated labels and their designated objects has compensated for poorer stereoscopic text legibility. Future testing with dynamic scenes is expected to more clearly distinguish the three label separation techniques.