Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.
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, Caldera Christian, Krispel Ulrich, Eggeling Eva, Sunk Alexander, Reisinger Gerhard, Sihn Wilfried, Fellner Dieter W.
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 map – is still created using pen & paper by physically visiting the production line. 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 as a future state map by using a meta description together with a dependency graph. With VASCO we present a tool, which contributes to all parts of value stream analysis - from data acquisition, over analyzing, planning, comparison up to simulation of alternative future state maps.We call this a holistic approach for Value stream mapping including detailed analysis of lead time, productivity, space, distance, material disposal, energy and carbon dioxide equivalents – depending in a change of calculated direct product costs.
Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.
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: https://www.researchgate.net/publication/309479681_Visual_Exploration_of_Hierarchical_Data_Using_Degree-of-Interest_Controlled_by_Eye-Tracking [accessed Oct 3, 2017].
Berndt Rene, Silva Nelson, Edtmayr Thomas, Sunk Alexander, Krispel Ulrich, Caldera Christian, Eggeling Eva, Fellner Dieter W., Sihn Wilfried
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.