We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.

TitelProceedings - ETRA 2018
Untertitel2018 ACM Symposium on Eye Tracking Research and Applications
Herausgeber (Verlag)Association of Computing Machinery
ISBN (elektronisch)9781450357067
PublikationsstatusVeröffentlicht - 2018
Veranstaltung10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018 - Warsaw, Polen
Dauer: 14 Jun 201817 Jun 2018


Konferenz10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018

ASJC Scopus subject areas

  • !!Computer Vision and Pattern Recognition
  • Human-computer interaction
  • !!Ophthalmology
  • !!Sensory Systems

Fields of Expertise

  • Information, Communication & Computing

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