Abstract
During eye-tracking experiments, a large amount of data is collected. Usually, it is stored in text files, or file formats meant for scientific data. Because these formats provide limited sets of mechanisms for more comprehensive data analysis, various database models were considered as an alternative for them in this study. They included a relational database defined in MS SQL Server and two commonly used NoSQL solutions – MongoDB and Cassandra. The data structures defined for each of them and their performance were compared based on a chosen set of operations. A special multi-database platform was developed, enabling access to all database models without knowing their mechanisms for manipulating data. The studies revealed that Cassandra is the best database to store eye-tracking data when adjusted to the data retrieving operations. MongoDB databases featured with similar performance for various query types, yet with worse execution time. The worst results were obtained for the relational database. Designed to avoid redundancy by assumption, it stores data in many tables, and many join operations are needed while reading data. This fact entails weaker operations efficiency.
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