Georges G. Grinstein

Iconographic Displays For Visualizing Multidimensional Data

Steeply increasing amounts and complexity of scientific data demand improved capability to display data graphically. A powerful new graphic technique for displaying multidimensional data is explained and illustrated. The basic approach is to represent each datum by a graphic icon, the visible features of which are under control of the multiple measures on each datum. W n the icons are displayed en masse, densely stacked into a two-dimensional array, statistical structure in the data is perceived in the form of texture contours or gradients of texture variation over the display. This approach is illustrated with weather satellite imagery data. Five channels of multispectral data are combined into one picture, in which each pixel is an icon. We also describe how large statistical data bases like medical epidemiological data or census data might be visualized iconographically.

DNA visual and analytic data mining

Describes data exploration techniques designed to classify DNA sequences. Several visualization and data mining techniques were used to validate and attempt to discover new methods for distinguishing coding DNA sequences (exons) from non-coding DNA sequences (introns). The goal of the data mining was to see whether some other, possibly non-linear combination of the fundamental position-dependent DNA nucleotide frequency values could be a better predictor than the AMI (average mutual information). We tried many different classification techniques including rule-based classifiers and neural networks. We also used visualization of both the original data and the results of the data mining to help verify patterns and to understand the distinction between the different types of data and classifications. In particular, the visualization helped us develop refinements to neural network classifiers, which have accuracies as high as any known method. Finally, we discuss the interactions between visualization and data mining and suggest an integrated approach.

Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations

We introduce a graphic primitive, called a dimensional anchor (DA), which facilitates the creation of new visualizations and provides insight into the analysis of information visualizations. The DA represents an attempt to provide a unified framework or model for a variety of visualizations, including Parallel Coordinates, scatter plot matrices, Radviz, Survey Plots and Circle Segments A dimensional anchor is constructed by assigning values to parameters associated with various geometric graphic elements that encode the basics of the above visualizations. We define a visualization vector space in which all of the above visualizations and many new ones are represented by vectors. These encodings make it possible to perform a Grand Tour traveling from Parallel Coordinates to Survey Plot, and visiting many other visualizations in between

Promoting Insight-Based Evaluation of Visualizations: From Contest to Benchmark Repository

Information visualization (InfoVis) is now an accepted and growing field, but questions remain about the best uses for and the maturity of novel visualizations. Usability studies and controlled experiments are helpful, but generalization is difficult. We believe that the systematic development of benchmarks will facilitate the comparison of techniques and help identify their strengths under different conditions. We were involved in the organization and management of three InfoVis contests for the 2003, 2004, and 2005 IEEE InfoVis Symposia, which requested teams to report on insights gained while exploring data. We give a summary of the state of the art of evaluation in InfoVis, describe the three contests, summarize their results, discuss outcomes and lessons learned, and conjecture the future of visualization contests. All materials produced by the contests are archived in the InfoVis benchmark repository.

A Survey of 3D Solid Reconstruction from 2D Projection Line Drawings

The reconstruction of a 3D object from its 2D projection(s) and its corresponding problem of 3D object recognition are two of the important research areas in the field of computer vision and artificial intelligence. Reconstruction involves determining the geometric and topological relationship of an objects atomic parts whereas recognition involves identifying an object by some form of template matching. Nagendra and Gujar1 gave a survey of several papers on reconstruction of 3D object from its 2D views. In this paper we present a taxonomy of 3D object reconstruction from 2D projection line drawings. We base the classification on the number of 2D views of the 3D solid object, the degree of user interaction necessary for correct reconstruction, and the internal representation used in the reconstruction process. We discuss the basic issues associated with this problem, review the relevant literature and present topics for future research.

A universal visualization platform

Although there are a number of visualization systems to choose from when analyzing data, only a few of these allow for the integration of other visualization and analysis techniques. There are even fewer visualization toolkits and frameworks from which one can develop ones own visualization applications. Even within the research community, scientists either use what they can from the available tools or start from scratch to define a program in which they are able to develop new or modified visualization techniques and analysis algorithms. Presented here is a new general-purpose platform for constructing numerous visualization and analysis applications. The focus of this system is the design and experimentation of new techniques, and where the sharing of and integration with other tools becomes second nature. Moreover, this platform supports multiple large data sets, and the recording and visualizing of user sessions. Here we introduce the Universal Visualization Platform (UVP) as a modern data visualization and analysis system.

Vectorized Radviz and Its Application to Multiple Cluster Datasets

Radviz is a radial visualization with dimensions assigned to points called dimensional anchors (DAs) placed on the circumference of a circle. Records are assigned locations within the circle as a function of its relative attraction to each of the DAs. The DAs can be moved either interactively or algorithmically to reveal different meaningful patterns in the dataset. In this paper we describe Vectorized Radviz (VRV) which extends the number of dimensions through data flattening. We show how VRV increases the power of Radviz through these extra dimensions by enhancing the flexibility in the layout of the DAs. We apply VRV to the problem of analyzing the results of multiple clusterings of the same data set, called multiple cluster sets or cluster ensembles. We show how features of VRV help discern patterns across the multiple cluster sets. We use the Iris data set to explain VRV and a newt gene microarray data set used in studying limb regeneration to show its utility. We then discuss further applications of VRV.

Stereophonic and surface sound generation for exploratory data analysis

The analysis and interpretation of very high dimensional data require the development and use of data presentation techniques that harness human perceptual powers. The University of Lowells Exploratory Visualization project (Exvis) aims at designing, implementing, and evaluating perceptually-based tools for data presentation using both visual and auditory domains. This paper describes several auditory data presentation techniques, including the generation of stereophonic sound with apparent depth and sound that appears to emanate from a two-dimensional area. Both approaches can produce sound with auditory texture.

Identification and Management of Women at High Risk for Hereditary Breast/Ovarian Cancer Syndrome

Abstract:  Despite advances in identifying genetic markers of high risk patients and the availability of genetic testing, it remains challenging to efficiently identify women who are at hereditary risk and to manage their care appropriately. HughesRiskApps, an open‐source family history collection, risk assessment, and Clinical Decision Support (CDS) software package, was developed to address the shortcomings in our ability to identify and treat the high risk population. This system is designed for use in primary care clinics, breast centers, and cancer risk clinics to collect family history and risk information and provide the necessary CDS to increase quality of care and efficiency. This paper reports on the first implementation of HughesRiskApps in the community hospital setting. HughesRiskApps was implemented at the Newton‐Wellesley Hospital. Between April 1, 2007 and March 31, 2008, 32,966 analyses were performed on 25,763 individuals. Within this population, 915 (3.6%) individuals were found to be eligible for risk assessment and possible genetic testing based on the 10% risk of mutation threshold. During the first year of implementation, physicians and patients have fully accepted the system, and 3.6% of patients assessed have been referred to risk assessment and consideration of genetic testing. These early results indicate that the number of patients identified for risk assessment has increased dramatically and that the care of these patients is more efficient and likely more effective.

A human cognition framework for information visualization

Abstract We present a human cognition framework for information visualization. This framework emphasizes how top-down cognitive processing enables the induction of insight, reasoning, and understanding, which are key goals of the visual analytics community. Specifically, we present a set of six leverage points that can be exploited by visualization designers in order to measurably influence certain aspects of human cognition: (1) exogenous attention; (2) endogenous attention; (3) chunking; (4) reasoning with mental models; (5) analogical reasoning; and (6) implicit learning.