From when and what to where: Linking spatio-temporal visualizations in visual analytics

Chen, Yingjie Victor; Qian, Zhenyu

doi:10.1109/isi.2013.6578783

Cited by 1 publication

(1 citation statement)

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“…It demonstrates a solution to link the time series curves, geospatial visualizations, and data views together and to help the user achieve situational awareness through comprehension of the what, where, and when attributes of cybersecurity issues. This paper was originated from our previous work [8] that attempted to link the user from the temporal time series curve to geospatial visualizations. At this paper, we were able to extend the approach and its application to link the user in multiple directions among temporal visualization, geospatial visualization, and data view.…”

Section: Introductionmentioning

confidence: 99%

Fluency of visualizations: linking spatiotemporal visualizations to improve cybersecurity visual analytics

Qian

Chen

2014

Secur Inform

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This paper adopts the metaphor of representational fluency and proposes an auto linking approach to help analysts investigate details of suspicious sections across different cybersecurity visualizations. Analysis of spatiotemporal network security data takes place both conditionally and in sequence. Many visual analytics systems use time series curves to visualize the data from the temporal perspective and maps to show the spatial information. To identify anomalies, the analysts frequently shift across different visualizations and the original data view. We consider them as various representations of the same data and aim to enhance the fluency of navigation across these representations. With the auto linking mechanism, after the analyst selects a segment of a curve, the system can automatically highlight the related area on the map for further investigation, and the selections on the map or the data views can also trigger the related time series curves. This approach adopts the slicing operation of the Online Analytical Process (OLAP) to find the basic granularities that contribute to the overall value change. We implemented this approach in an award-winning visual analytics system, SemanticPrism, and demonstrate the functions through two use cases.

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Section: Introductionmentioning

confidence: 99%