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SeeME: A General, Reusable Graph Schema for Data Preprocessing of Eye-Tracking Data
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2025
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Gesellschaft für Informatik, Bonn
Zusammenfassung
To track eye movement over time and to gain information about points of interest through fixation data, eye-tracking is used in a wide range of fields. In this paper, we present a general, reusable approach to store eye-tracking data and to realize data preprocessing tasks in-database. To achieve this, a graph databases graph schema for any eye-tracking data, consisting of 1) a time series data level and 2) a meta level is developed. Follow-up experiments or additional data like demographic data can easily be integrated into the meta level of the general schema. We use Neo4j to implement this general graph schema. To prepare the time series data for machine learning tasks we additionally present a modular in-graph-database preprocessing pipeline, empowering researchers to either compare different operators or select the best fitting one. For each preprocessing step Cypher code for at least two preprocessing algorithms for time series are at hand.