Temporal Event Sequence Simplification

By Megan Monroe, Rongjian Lan, Hanseung Lee, Catherine Plaisant, Ben Shneiderman. IEEE Transactions on Visualization and Computer Graphics, 2013

310 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/72d8bab8729f336c713d7474ff5f8eaa5deeb5be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Shixia Liu
2. Weiwei Cui
3. Ben Shneiderman
4. Catherine Plaisant
5. Neil T. Spring
6. Kenyon Crowley
7. Fan Du
8. Panpan Xu

In Signal Processing

1. Neil T. Spring
2. Liu Ren
3. Ben Shneiderman
4. Chris Callison-Burch
5. Catherine Plaisant
6. Weiwei Cui
7. Kenyon Crowley
8. Fan Du

Citations in the corpus

• Sequence Synopsis: Optimize Visual Summary of Temporal Event Data (2018, methodology): “Using color to encode event type is a common practice in event sequence visualization [26,29].”
• How Hierarchical Topics Evolve in Large Text Corpora (2014, background): “[26] developed a set of user-driven data implication techniques to help users understand large temporal event records.”
• EventAction (2019, methodology): “Unlike many other event sequence visualization tools [24, 50, 82, 83], the analytical workflow of EventAction is oriented by a seed record.”
• A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk (2017, methodology): “[50] that groups a series of temporally close time intervals into clusters using an interval threshold, D.”