Collecting and Harnessing Rich Session Histories

Goodell, Howard; Chiang, Chih‐Hung; Kelleher, Curran; Baumann, Alexander; Grinstein, Georges

doi:10.1109/iv.2006.33

Cited by 8 publications

(11 citation statements)

References 6 publications

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“…Bookmarking [11,14], keyword tagging, text comments [10,11,16,24], and audio annotations [10] are all potentially useful. Usage scenarios for visualization include analysts creating bookmarks for important findings [14], leaving text notes to describe a view to a collaborator [16], and recording audio annotations to aid "think-aloud" evaluation protocols [10].…”

Section: Metadata and Annotationmentioning

confidence: 99%

“…Metadata such as time, action type, bookmarks, and annotations are all potential search domains. Although filtering tools have been provided for web design histories [16], most visualization histories [3,6,10,18,19,20,25] lack search capabilities. A notable exception is VisTrails [4], which enables querying-by-example to find related visual exploration sessions across multiple users.…”

Section: Search and Filtermentioning

confidence: 99%

“…To design our history tools, we first conducted a design space analysis to enumerate these decisions. We surveyed prior work spanning general history mechanisms [2,8,9,23,29] and interface designs in the areas of graphical design tools [8,16,17,22,26], web browsing [1,5,12,14,15,30], and visualization and simulation [3,4,6,10,11,13,18,19,20,24,25]. In this section, we outline the design space of history tools using examples from this body of work.…”

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confidence: 99%

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Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation

Heer

Mackinlay

Stolte

et al. 2008

IEEE Trans. Visual. Comput. Graphics

252

229

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Abstract-Interactive history tools, ranging from basic undo and redo to branching timelines of user actions, facilitate iterative forms of interaction. In this paper, we investigate the design of history mechanisms for information visualization. We present a design space analysis of both architectural and interface issues, identifying design decisions and associated trade-offs. Based on this analysis, we contribute a design study of graphical history tools for Tableau, a database visualization system. These tools record and visualize interaction histories, support data analysis and communication of findings, and contribute novel mechanisms for presenting, managing, and exporting histories. Furthermore, we have analyzed aggregated collections of history sessions to evaluate Tableau usage. We describe additional tools for analyzing users' history logs and how they have been applied to study usage patterns in Tableau.Index Terms-Visualization, history, undo, analysis, presentation, evaluation. INTRODUCTIONWhen investigating data with visualizations, users regularly traverse the space of views in an iterative fashion. Exploratory analysis may result in a number of hypotheses, leading to multiple rounds of question-answering. Analysts can generate unexpected questions that may be investigated immediately or revisited later. After conducting analysis, users may need to review, summarize, and communicate their findings, often in the form of reports or presentations. By surfacing users' interaction history, we can facilitate analysis and communication. History mechanisms such as undo or "timetravel" enable revisitation in a variety of applications (e.g., [1-4, 6, 8, 12-14, 16-20, 22, 24-26]). As noted by Shneiderman [27], such history tools can play an important part in the visualization process, supporting iterative analysis by enabling users to review, retrieve, and revisit visualization states. Moreover, history tools can help users create reports or presentations, facilitating communication.Interaction histories can also benefit research and development. History log analysis of both individual and aggregate usage can identify common usage patterns and thereby assist usability evaluation. Researchers can also study interaction patterns to better understand and model analysts' sense-making process [13].However, the best history mechanisms for achieving these benefits are not always clear. Designers of visualization tools must consider a large design space of potential features and system architectures when designing history tools. These design decisions entail trade-offs in the types of history representations and operations that can be provided.For example, while it is easy to log low-level input events such as key presses and mouse clicks [25], users can more readily take advantage of semantically meaningful models. Many operations might be performed on an interaction history, including editing, aggregation, bookmarking, annotation, and search. Architecture and interface design need to account for such operations. ...

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Section: Metadata and Annotationmentioning

confidence: 99%

Section: Search and Filtermentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation

Heer

Mackinlay

Stolte

et al. 2008

IEEE Trans. Visual. Comput. Graphics

252

229

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show abstract

“…The process could be viewed as a branching tree (there are systems where analysis sessions can be explicitly visualized in this way, e.g. Goodell et al 2006).…”

Section: Sensitivity Issuesmentioning

confidence: 99%

Visually driven analysis of movement data by progressive clustering

Rinzivillo

Pedreschi

Nanni

et al. 2008

Information Visualization

172

129

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, i.e. sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, first, is difficult to build, second, would require much time to compute, third, might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of "cheap" distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real dataset. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories.

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“…User actions are recorded as changes to system state. Each action is categorized in a data exploration taxonomy, so state changes anywhere in the system can be compared in a principled way [2,3]. Metadata including audio and text annotations is associated with the states in which it was recorded.…”

Section: Visual History Metricsmentioning

confidence: 99%

Metrics for analyzing rich session histories

Goodell

Chiang

Kelleher

et al. 2006

Proceedings of the 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization

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To be most useful, evaluation metrics should be based on detailed observation and effective analysis of a full spectrum of system use. Because observation is costly, ideally we want a system to provide in-depth data collection with allied analyses of the key user interface elements. We have developed a visualization and analysis platform [1] that automatically records user actions and states at a high semantic level [2 and 3], and can be directly restored to any state. Audio and text annotations are collected and indexed to states, allowing users to comment on their current situation as they work, and/or as they review the session. These capabilities can be applied to usability evaluation of the system, describing problems they encountered, or to suggest improvements to the environment. Additionally, computed metrics are provided at each state [3, 4, and 5]. We believe that the metrics and the associated history data will allow us to deduce patterns of data exploration, to compare users, to evaluate tools, and to understand in a more automated approach the usability of the visualization system as a whole.

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Collecting and Harnessing Rich Session Histories

Cited by 8 publications

References 6 publications

Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation

Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation

Visually driven analysis of movement data by progressive clustering

Metrics for analyzing rich session histories

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