Optimizing ASReview simulations: A generic multiprocessing solution for ‘light-data’ and ‘heavy-data’ users

Abstract

Active learning can be used for optimizing and speeding up the screening phase of systematic reviews. Running simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training data. This paper presents an architecture design with a multiprocessing computational strategy for running many such simulation studies in parallel, using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud technologies. We provide a technical explanation of the proposed cloud architecture and its usage. In addition to that, we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM settings. Our analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing usage. The parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and, at the same time, ensure the safe completion of the needed processes.