Design and analysis of a relational database for behavioral experiments data processing

Abstract

This paper presents the results of a comparative analysis between different approaches to experimental data storage and processing. Several studies related to the problem and some methods for solving it have been discussed. Different types of databases, ways of using them and the areas of their application are analyzed. For the purposes of the study, a relational database for storing and analyzing specific data from behavioral experiments was designed. The methodology and conditions for conducting the experiments are described. Three different indicators are analyzed, respectively: memory required to store the data, time to load the data from an external file into computer memory and iteration time across all records through one cycle. The obtained results show that for storing a large number of records (in the order of tens of millions rows) either dynamic arrays (stored on external media in binary file format), or an approach based on a local or remote database management system can be used. Regarding the data loading time, the fastest approach was the one that uses dynamic arrays. It outperforms significantly the approaches based on a local or remote database. The obtained results show that the dynamic arrays and the local data sets approaches iterated much faster across all data records than the remote database approach.