Editorial

Abstract

The international workshop on Big Data-Driven Edge Cloud Services (BECS) aims to provide a platform for scholars and practitioners to share their experiences and present ongoing work in developing data-driven AI appli- cations and services within distributed computing environments, commonly referred to as the edge cloud.

The fourth edition of the workshop (BECS 2024)1 was held in conjunc- tion with the 24th International Conference on Web Engineering (ICWE 2024)2, which took place in Tampere, Finland, from 17–20 June 2024. This special issue of the Journal of Web Engineering focuses on enhancing the efficiency of machine learning (ML)-based systems by leveraging the unique features of distributed edge cloud environments. For this issue, we selected papers from BECS 2024 that propose conceptual frameworks to improve the performance and privacy of ML-based systems and explore distributed ML-based solutions for addressing real-world challenges.