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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In this paper, we present a new approach for the visualization of time-series data based on spirals. Different to classical bar charts and line graphs, the spiral is suited to visualize large data sets and supports much better the identification of periodic structures in the data. Moreover, it supports both the visualization of nominal and quantitative data based on a similar visualization metaphor. The extension of the spiral visualization to 3D gives access to concepts for zooming and focusing and linking in the data set. As such, spirals complement other visualization techniques for time series and specifically enhance the identication of periodic patterns. Slik lyder tre av omtalene av Edward Tuftes The visual display of quantitative information. Siden forste utgave kom ut i 1983, har den blitt sett pa som en tidlos klassiker og bestselger innen informasjonsgrafikk. Siden har den hatt tre «oppfolgere»: Envisioning information (1990), Visual explanations (1997) og Beautiful Evidence (2006). Med sin hoye kompetanse innen informasjonsgrafikk blir Edward Tufte i dag sett pa som en av de fremste pioneerene innen faget, og han har blitt tildelt over 40 priser for sine verker. Han er professor ved Yale University, og underviser den dag i dag i statistikk, informasjonsdesign og politisk okonomi. I The visual display of quantitative information drofter Tufte med et kritisk syn flere eksempler pa informasjonsgrafikk skapt gjennom tidene. Budskapet summeres opp i klare retningslinjer pa hva som er rett og galt innen faget – hvordan man skal visualisere kvantitativ informasjon korrekt. Boken har derfor blitt tatt godt i mot hos flere yrkesgrupper – den har naermest blitt sett pa som en bibel – dog har den og opplevd mye kritikk selv. We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM KDD process, using it to enhance user interaction and comprehension. Timeboxes are rectangular widgets that can be used in direct-manipulation graphical user interfaces (GUIs) to specify query constraints on time series data sets. Timeboxes are used to specify simultaneously two sets of constraints: given a set of N time series profiles, a timebox covering time periods x1...x2 (x1<x2) and values y1...y2(y1≤y2) will retrieve only those n ∈N that have values y1≤y≤y2 during all times x1≤x≤x2. TimeSearcher is an information visualization tool that combines timebox queries with overview displays, query-by-example facilities, and support for queries over multiple time-varying attributes. Query manipulation tools including pattern inversion and 'leaders & laggards' graphical bookmarks provide additional support for interactive exploration of data sets. Extensions to the basic timebox model that provide additional expressivity include variable time timeboxes, which can be used to express queries with variability in the time interval, and angular queries, which search for ranges of differentials, rather than absolute values. Analysis of the algorithmic requirements for providing dynamic query performance for timebox queries showed that a sequential search outperformed searches based on geometric indices. Design studies helped identify the strengths and weaknesses of the query tools. Extended case studies involving the analysis of two different types of data from molecular biology experiments provided valuable feedback and validated the utility of both the timebox model and the TimeSearcher tool. Timesearcher is available at http: www.cs.umd.edu hcil timesearcher