Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users  

作　　者：Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot 

机构地区：[1]Applied Data Science,Department of Information and Computing Science,Faculty of Science,Utrecht University [2]Netherlands eScienceCenter,Amsterdam,NL [3]Department of Research and Data Management Services,Information Technology Services,Utrecht University,Utrecht,the Netherlands [4]Department of Methodology and Statistics,Faculty of Social and Behavioral Sciences,Utrecht University

出　　处：《Data Intelligence》2024年第2期320-343,共24页数据智能（英文）

基　　金：supported by the Netherlands eScience Center under grant number ODISSEI.2022.023。

摘　　要：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 withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,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.

关 键 词：Active learning Systematic review Simulation study MULTIPROCESSING Cloud architecture 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]