Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Silva Nelson, Schreck Tobias, Veas Eduardo Enrique, Sabol Vedran, Eggeling Eva, Fellner Dieter W.

Leveraging Eye-gaze and Time-series Features to Predict User Interests and Build a Recommendation Model for Visual Analysis

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

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.

Shao Lin, Silva Nelson, Schreck Tobias, Eggeling Eva

Visual Exploration of Large Scatter Plot Matrices by Pattern Recommendation based on Eye Tracking

ESIDA 2017 - Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, co-located with IUI 2017 - International Conference on Intelligent User Interfaces, ACM, Limassol, Cyprus, 2017

The Scatter Plot Matrix (SPLOM) is a well-known technique for visual analysis of high-dimensional data. However, one problem of large SPLOMs is that typically not all views are potentially relevant to a given analysis task or user. The matrix itself may contain structured patterns across the dimensions, which could interfere with the investigation for unexplored views. We introduce a new concept and prototype implementation for an interactive recommender system supporting the exploration of large SPLOMs based on indirectly obtained user feedback from user eye tracking. Our system records the patterns that are currently under exploration based on gaze times, recommending areas of the SPLOM containing potentially new, unseen patterns for successive exploration. We use an image-based dissimilarity measure to recommend patterns that are visually dissimilar to previously seen ones, to guide the exploration in large SPLOMs. The dynamic exploration process is visualized by an analysis provenance heatmap, which captures the duration on explored and recommended SPLOM areas. We demonstrate our exploration process by a user experiment, showing the indirectly controlled recommender system achieves higher pattern recall as compared to fully interactive navigation using mouse operations.

Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W. - Open Source Framework for the Exploration and Visualization of Eye Tracking Data

IEEEVis - Proc. IEEE Conference on Information Visualization, Baltimore, Maryland, USA, 2016

We present a new open-source prototype framework to exploreand visualize eye-tracking experiments data. Firstly, standard eyetrackersare used to record raw eye gaze data-points on user experiments.Secondly, the analyst can configure gaze analysis parameters,such as, the definition of areas of interest, multiple thresholdsor the labeling of special areas, and we upload the data to a searchserver. Thirdly, a faceted web interface for exploring and visualizingthe users’ eye gaze on a large number of areas of interest isavailable. Our framework integrates several common visualizationsand it also includes new combined representations like an eye analysisoverview and a clustered matrix that shows the attention timestrength between multiple areas of interest. The framework can bereadily used for the exploration of eye tracking experiments data.We make available the source code of our prototype framework foreye-tracking data analysis.

Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.

Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking

FMT 2016 : 9th Forum Media Technology 2016, Wolfgang Aigner , Grischa Schmiedl , Kerstin Blumenstein , Matthias Zeppelzauer , Michael Iber, St. Pölten, 2016

Effective visual exploration of large data sets is an important problem. A standard tech- nique for mapping large data sets is to use hierarchical data representations (trees, or dendrograms) that users may navigate. If the data sets get large, so do the hierar- chies, and effective methods for the naviga- tion are required. Traditionally, users navi- gate visual representations using desktop in- teraction modalities, including mouse interac- tion. Motivated by recent availability of low- cost eye-tracker systems, we investigate ap- plication possibilities to use eye-tracking for controlling the visual-interactive data explo- ration process. We implemented a proof-of- concept system for visual exploration of hier- archic data, exemplified by scatter plot dia- grams which are to be explored for grouping and similarity relationships. The exploration includes usage of degree-of-interest based dis- tortion controlled by user attention read from eye-movement behavior. We present the basic elements of our system, and give an illustra- tive use case discussion, outlining the applica- tion possibilities. We also identify interesting future developments based on the given data views and captured eye-tracking information. (13) Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking. Available from: [accessed Oct 3, 2017].

Berndt Rene, Silva Nelson, Edtmayr Thomas, Sunk Alexander, Krispel Ulrich, Caldera Christian, Eggeling Eva, Fellner Dieter W., Sihn Wilfried

VASCO - Mastering the Shoals of Value Stream Mapping

CONTENT 2016, The Eighth International conference on Creative Content Technologies, Hans-Werner Sehring, René Berndt, IARIA, Rome, Italy, 2016

Value stream mapping is a lean management method for analyzing and optimizing a series of events for production or services. Even today the first step in value stream analysis - the acquisition of the current state - is still created using pen & paper by physically visiting the production place. We capture a digital representation of how manufacturing processes look like in reality. The manufacturing processes can be represented and efficiently analyzed for future production planning by using a meta description together with a dependency graph. With our Value Stream Creator and explOrer (VASCO) we present a tool, which contributes to all parts of value stream analysis - from data acquisition, over planning, comparison with previous realities, up to simulation of future possible states.

Silva Nelson, Settgast Volker, Eggeling Eva, Grill Florian, Zeh Theodor, Fellner Dieter W.

Sixth Sense - Air Traffic Control Prediction Scenario Augmented by Sensors

I-Know 2014, 2014

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