Data modelling and data processing generated by human eye movements

Abstract

Data modeling and data processing are important activities in any scientific research. This research focuses on the modeling of data and processing of data generated by a saccadometer. The approach used is based on the relational data model, but the processing and storage of the data is done with client datasets. The experiments were performed with 26 randomly selected files from a total of 264 experimental sessions. The data from each experimental session was stored in three different formats, respectively text, binary and extensible markup language (XML) based. The results showed that the text format and the binary format were the most compact. Several actions related to data processing were analyzed. Based on the results obtained, it was found that the two fastest actions are respectively loading data from a binary file and storing data into a binary file. In contrast, the two slowest actions were storing the data in XML format and loading the data from a text file, respectively. Also, one of the time-consuming operations turned out to be the conversion of data from text format to binary format. Moreover, the time required to perform this action does not depend in proportion on the number of records processed.