Interest Driven Navigation in Visualization

Healey, Christopher G.; Dennis, Brent M.

doi:10.1109/tvcg.2012.23

Cited by 17 publications

(16 citation statements)

References 37 publications

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“…Relevance feedback can be applied to predict relevance of previously unseen scatter plot views, effectively narrowing the search space [BKSS14]. Healey and Dennis [HD12] train a classifier from user‐selected views in a larger geospatial data set, supporting navigation to unexplored data areas. Finally, in own previous work we train classifiers to find potentially interesting scenes in soccer matches based on trajectory features [JSS*14] and explicit user feedback.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Visual Abstraction of Soccer Movement

Sacha

Al-Masoudi

Stein

et al. 2017

Computer Graphics Forum

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractTrajectory-based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on-the-fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi-automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer.

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Section: Related Workmentioning

confidence: 99%

Dynamic Visual Abstraction of Soccer Movement

Sacha

Al-Masoudi

Stein

et al. 2017

Computer Graphics Forum

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“…The assumption is that the additionally retrieved data will add to the user information need. In [16], user data navigation is supported by a Bayes classification approach. The method learns to distinguish between interesting and uninteresting data sections while users pan and zoom an information landscape.…”

Section: Relevance-driven Analytics and Distinction Of Our Workmentioning

confidence: 99%

“…Our approach is novel in that we a) introduce the concept of relevance feedback to scatter plot exploration, and b) that we make explicit the gained knowledge by a decision tree, which is used to guide and monitor the user exploration process. In that, our approach is related to [16] where a Bayes classifier is used to navigate a 2D information landscape. Our approach differs from [4,12] in that the latter works consider user feedback in one single 2D view of the data, which is continuously updated.…”

Section: Relevance-driven Analytics and Distinction Of Our Workmentioning

confidence: 99%

Feedback-driven interactive exploration of large multidimensional data supported by visual classifier

Behrisch

Korkmaz

Shao

et al. 2014

2014 IEEE Conference on Visual Analytics Science and Technology (VAST)

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Abstract-The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user-and context-dependent visual exploration systems.

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“…A better solution would be to track an analyst's actions to better anticipate their strategies for specific types of tasks, for example, what types of data are they likely to request, how do they prefer that data to be visualized, and so on. We have previously used preference elicitation algorithms from artificial intelligence to track an analyst's interests within a visualization session [6]. We believe a similar approach can be used to determine analysts' preferences across multiple sessions.…”

Section: Future Workmentioning

confidence: 99%

Flexible web visualization for alert-based network security analytics

Hao

Healey

Hutchinson

2013

Proceedings of the Tenth Workshop on Visualization for Cyber Security

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This paper describes a web-based visualization system designed for network security analysts at the U.S. Army Research Laboratory (ARL). Our goal is to provide visual support to the analysts as they investigate security alerts for malicious activity within their systems. Our ARL collaborators identified a number of important requirements for any candidate visualization system. These relate to the analyst's mental models and working environment, and to the visualization tool's configurability, accessibility, scalability, and "fit" with existing analysis strategies. To meet these requirements, we designed and implement a web-based tool that uses different types of charts as its core representation framework. A JavaScript charting library (RGraph) was extended to provide the interface flexibility and correlation capabilities needed to support analysts as they explore different hypotheses about a potential attack. We describe key elements of our design, explain how an analyst's intent is used to generate different visualizations, and show how the system's interface allows an analyst to rapidly produce a sequence of visualizations to explore specific details about a potential attack as they arise. We conclude with a discussion of plans to further improve the system, and to collect feedback from our ARL colleagues on its strengths and limitations in real-world analysis scenarios.

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Interest Driven Navigation in Visualization

Cited by 17 publications

References 37 publications

Dynamic Visual Abstraction of Soccer Movement

Dynamic Visual Abstraction of Soccer Movement

Feedback-driven interactive exploration of large multidimensional data supported by visual classifier

Flexible web visualization for alert-based network security analytics

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