Unlike static documents, version-controlled documents are edited by one or more authors over a certain period of time. Examples include large scale computer code, papers authored by a team of scientists, and online discussion boards. Such collaborative revision process makes traditional document modeling and visualization techniques inappropriate. In this paper we propose a new visualization technique for version-controlled documents that reveals interesting authoring patterns in papers, computer code and Wikipedia articles. The revealed authoring patterns are useful for the readers, participants in the authoring process, and supervisors.
Visualizing numeric data, such as word histograms, serves a foundational role in visualizing complicated textual objects. Monographs describing traditional visualization techniques are @cite_5 @cite_17 while less traditional approaches for visual data exploration are surveyed in @cite_24 . Some interesting ideas concerning visualizing low-dimensional numeric time series are @cite_1 @cite_11 . Recent trends in the area of time series visualization are mostly concerned with interactive visualization and with multiple or vector-valued time series. An interesting exposition of the state-of-the-art and future vision in the related field of visual analytics is @cite_27 .
@cite_1 @cite_17 @cite_24 @cite_27 @cite_5 @cite_11
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Documents and other categorical valued time series are often characterized by the frequencies of short range sequential patterns such as n-grams. This representation converts sequential data of varying lengths to high dimensional histogram vectors which are easily modeled by standard statistical models. Unfortunately, the histogram representation ignores most of the medium and long range sequential dependencies making it unsuitable for visualizing sequential data. We present a novel framework for sequential visualization of discrete categorical time series based on the idea of local statistical modeling. The framework embeds categorical time series as smooth curves in the multinomial simplex summarizing the progression of sequential trends. We discuss several visualization techniques based on the above framework and demonstrate their usefulness for document visualization. Time is an exceptional dimension that is common to many application domains such as medicine, engineering, business, or science. Due to the distinct characteristics of time, appropriate visual and analytical methods are required to explore and analyze them. This book starts with an introduction to visualization and historical examples of visual representations. At its core, the book presents and discusses a systematic view of the visualization of time-oriented data along three key questions: what is being visualized (data), why something is visualized (user tasks), and how it is presented (visual representation). To support visual exploration, interaction techniques and analytical methods are required that are discussed in separate chapters. A large part of this book is devoted to a structured survey of 101 different visualization techniques as a reference for scientists conducting related research as well as for practitioners seeking information on how their time-oriented data can best be visualized. The usage of visualizations to aid the analysis of time oriented data plays an important role in various fields of applications. The need to visualize such data was decisive for the development of different visualization techniques over the last years. One of the frequently applied techniques is animation in order to illustrate the movements in such a way to make changes in the data transparent. However, evaluation studies of such animated interfaces for time-oriented data with potential users are still difficult to find. In this paper, we present our observations based on a systematic literature review with the motivation to support researchers and designers to identify future directions for their research. The literature review is split in two parts: (1) research on animation from the field of psychology, and (2) evaluation studies with the focus on animation of time-oriented data. Reading a visualization can involve a number of tasks such as extracting, comparing or aggregating numerical values. Yet, most of the charts that are published in newspapers, reports, books, and on the Web only support a subset of these tasks. In this paper we introduce graphical overlays-visual elements that are layered onto charts to facilitate a larger set of chart reading tasks. These overlays directly support the lower-level perceptual and cognitive processes that viewers must perform to read a chart. We identify five main types of overlays that support these processes; the overlays can provide (1) reference structures such as gridlines, (2) highlights such as outlines around important marks, (3) redundant encodings such as numerical data labels, (4) summary statistics such as the mean or max and (5) annotations such as descriptive text for context. We then present an automated system that applies user-chosen graphical overlays to existing chart bitmaps. Our approach is based on the insight that generating most of these graphical overlays only requires knowing the properties of the visual marks and axes that encode the data, but does not require access to the underlying data values. Thus, our system analyzes the chart bitmap to extract only the properties necessary to generate the desired overlay. We also discuss techniques for generating interactive overlays that provide additional controls to viewers. We demonstrate several examples of each overlay type for bar, pie and line charts.