Searching the Visual Style and Structure of D3 Visualizations.

We present a search engine for D3 visualizations that allows queries based on their visual style and underlying structure. To build the engine we crawl a collection of 7860 D3 visualizations from the Web and deconstruct each one to recover its data, its data-encoding marks and the encodings describing how the data is mapped to visual attributes of the marks. We also extract axes and other non-data-encoding attributes of marks (e.g., typeface, background color). Our search engine indexes this style and structure information as well as metadata about the webpage containing the chart. We show how visualization developers can search the collection to find visualizations that exhibit specific design characteristics and thereby explore the space of possible designs. We also demonstrate how researchers can use the search engine to identify commonly used visual design patterns and we perform such a demographic design analysis across our collection of D3 charts. A user study reveals that visualization developers found our style and structure based search engine to be significantly more useful and satisfying for finding different designs of D3 charts, than a baseline search engine that only allows keyword search over the webpage containing a chart.

An Analysis of Automated Visual Analysis Classification: Interactive Visualization Task Inference of Cancer Genomics Domain Experts.

We show how mouse interaction log classification can help visualization toolsmiths understand how their tools are used "in the wild" through an evaluation of MAGI - a cancer genomics visualization tool. Our primary contribution is an evaluation of twelve visual analysis task classifiers, which compares predictions to task inferences made by pairs of genomics and visualization experts. Our evaluation uses common classifiers that are accessible to most visualization evaluators: -nearest neighbors, linear support vector machines, and random forests. By comparing classifier predictions to visual analysis task inferences made by experts, we show that simple automated task classification can have up to 73 percent accuracy and can separate meaningful logs from "junk" logs with up to 91 percent accuracy. Our second contribution is an exploration of common MAGI interaction trends using classification predictions, which expands current knowledge about ecological cancer genomics visualization tasks. Our third contribution is a discussion of how automated task classification can inform iterative tool design. These contributions suggest that mouse interaction log analysis is a viable method for (1) evaluating task requirements of client-side-focused tools, (2) allowing researchers to study experts on larger scales than is typically possible with in-lab observation, and (3) highlighting potential tool evaluation bias.

The Color Wheel: A Heuristic Aid in Classification of a Skeletal Pattern.

Reading a complete print-out of cephalometric analysis expressed numerically consumes a fair amount of our time. The present paper addresses this issue by describing the "color wheel," a computerized procedure that attributes to the patient a single color representing type and severity of a skeletalproblem.

Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives.

While information visualization frameworks and heuristics have traditionally been reluctant to include acquired codes of meaning, designers are making use of them in a wide variety of ways. Acquired codes leverage a user's experience to understand the meaning of a visualization. They range from figurative visualizations which rely on the reader's recognition of shapes, to conventional arrangements of graphic elements which represent particular subjects. In this study, we used content analysis to codify acquired meaning in visualization. We applied the content analysis to a set of infographics and data visualizations which are exemplars of innovative and effective design. 88% of the infographics and 71% of data visualizations in the sample contain at least one use of figurative visualization. Conventions on the arrangement of graphics are also widespread in the sample. In particular, a comparison of representations of time and other quantitative data showed that conventions can be specific to a subject. These results suggest that there is a need for information visualization research to expand its scope beyond perceptual channels, to include social and culturally constructed meaning. Our paper demonstrates a viable method for identifying figurative techniques and graphic conventions and integrating them into heuristics for visualization design.

Solving graph data issues using a layered architecture approach with applications to web spam detection.

This paper proposes the combination of two state-of-the-art algorithms for processing graph input data, viz., the probabilistic mapping graph self organizing map, an unsupervised learning approach, and the graph neural network, a supervised learning approach. We organize these two algorithms in a cascade architecture containing a probabilistic mapping graph self organizing map, and a graph neural network. We show that this combined approach helps us to limit the long-term dependency problem that exists when training the graph neural network resulting in an overall improvement in performance. This is demonstrated in an application to a benchmark problem requiring the detection of spam in a relatively large set of web sites. It is found that the proposed method produces results which reach the state of the art when compared with some of the best results obtained by others using quite different approaches. A particular strength of our method is its applicability towards any input domain which can be represented as a graph.

Novel kernels for error-tolerant graph classification.

One of the major difficulties in graph classification is the lack of mathematical structure in the space of graphs. The use of kernel machines allows us to overcome this fundamental limitation in an elegant manner by addressing the pattern recognition problem in an implicitly existing feature vector space instead of the original space of graphs. In this paper we propose three novel error-tolerant graph kernels -- a diffusion kernel, a convolution kernel, and a random walk kernel. The kernels are closely related to one of the most flexible graph matching methods, graph edit distance. Consequently, our kernels are applicable to virtually any kind of graph. They also show a high degree of robustness against various types of distortion. In an experimental evaluation involving the classification of line drawings, images, diatoms, fingerprints, and molecules, we demonstrate the superior performance of the proposed kernels in conjunction with support vector machines over a standard nearest-neighbor reference method and several other graph kernels including a standard random walk kernel.

Digital imaging: an update.

UNLABELLED: It is becoming increasingly common for orthodontists and dentists to capture and store their patients' clinical records in a digital format, including photographs, radiographs and even study models. It is useful to have an understanding of what these image files are composed of in order to be able to manipulate, store and display the images efficiently to best effect. CLINICAL RELEVANCE: Digital images are becoming a routine part of patients'clinical records and it is important to understand their make-up and how they can be saved and displayed.

Which is better for presenting your data: table or graph?

This study aimed at investigating the characteristics of table and graph that people perceive and the data types which people consider the two displays are most appropriate for. Participants in this survey were 195 teachers and under-graduates from four universities in Beijing. The results showed people's different attitudes towards the two forms of display.

PubMatrix: a tool for multiplex literature mining.

BACKGROUND: Molecular experiments using multiplex strategies such as cDNA microarrays or proteomic approaches generate large datasets requiring biological interpretation. Text based data mining tools have recently been developed to query large biological datasets of this type of data. PubMatrix is a web-based tool that allows simple text based mining of the NCBI literature search service PubMed using any two lists of keywords terms, resulting in a frequency matrix of term co-occurrence. RESULTS: For example, a simple term selection procedure allows automatic pair-wise comparisons of approximately 1-100 search terms versus approximately 1-10 modifier terms, resulting in up to 1,000 pair wise comparisons. The matrix table of pair-wise comparisons can then be surveyed, queried individually, and archived. Lists of keywords can include any terms currently capable of being searched in PubMed. In the context of cDNA microarray studies, this may be used for the annotation of gene lists from clusters of genes that are expressed coordinately. An associated PubMatrix public archive provides previous searches using common useful lists of keyword terms. CONCLUSIONS: In this way, lists of terms, such as gene names, or functional assignments can be assigned genetic, biological, or clinical relevance in a rapid flexible systematic fashion. http://pubmatrix.grc.nia.nih.gov/

The classification of graphical elements.

In three experiments, participants classified stimuli depicting pie charts and stacked bar graphs on two criteria: a proportion shown in the graph, and the graph's overall size (scaling). Sorting times and errors were measured. For stacked bars, performance was impaired when participants sorted on the proportion and scaling varied. No such impairment occurred for pie charts. Experiment 1 showed that varying scaling produced Garner interference in classification of proportions with stacked bars, but not pies. Experiment 2 showed that this result held when the position of the pie slice was varied; Experiment 3 results showed facilitation for particular combinations of proportion and scaling levels. In general, the results showed that proportion and scaling had an asymmetric integral relation for stacked bar graphs, but were separable dimensions for pie charts.

An object-oriented taxonomy of medical data presentations.

A variety of methods have been proposed for presenting medical data visually on computers. Discussion of and comparison among these methods have been hindered by a lack of consistent terminology. A taxonomy of medical data presentations based on object-oriented user interface principles is presented. Presentations are divided into five major classes-list, table, graph, icon, and generated text. These are subdivided into eight subclasses with simple inheritance and four subclasses with multiple inheritance. The various subclasses are reviewed and examples are provided. Issues critical to the development and evaluation of presentations are also discussed.

Data display techniques.

The way data are displayed is as important as the data. The reader's ability to perceive, comprehend, and recall the information contained within the data is affected by how the data are presented. Multiple factors must be considered when designing data displays. Characteristics of the data and the intended use of the data should drive the choice of the display format. Formats for data displays can be words or numbers. Numbers can be displayed as specific values, tables, or graphs. Advantages and limitations of different types of data formats are presented. Techniques for the use of color are also included.