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.

Original languageEnglish
Title of host publicationProceedings - ETRA 2018
Subtitle of host publication2018 ACM Symposium on Eye Tracking Research and Applications
PublisherAssociation of Computing Machinery
ISBN (Electronic)9781450357067
Publication statusPublished - 2018
Event10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018 - Warsaw, Poland
Duration: 14 Jun 201817 Jun 2018


Conference10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018


  • Evaluation
  • Eye-tracking
  • Model
  • Recommend
  • Similarity
  • Time-series
  • Visual analytics

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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