Preface of the special issue on advances in data-driven engineering

Abstract

Disruptive changes in the industrial environment have occurred in recent years owing to rapid advancements in electronics, information, and communication technology. Because of the ever-increasing demands for product quality and economic benefit, intelligent components and devices are implemented, and networked and real-time supervision and control systems are also running in parallel. As a consequence, the level of automation in modern industrial systems is steadily rocketing. In addition, the increased availability of different data types paves the way to stunning scenarios for applying data-driven modeling techniques. The latter are revolutionizing complex systems’ modeling, prediction, and control. Fresh advances in scientific computing witness how data-driven methods can be applied to diverse, complex systems. Applications of Artificial intelligence-based systems play a pivotal role at the crossroads of almost all fields of computer science. Recent advances in big data generation and management have allowed decision-makers to utilize these overwhelming volumes of data for various purposes and analyses. This special issue consists of selected papers from an open call as well as thoroughly revised papers from the 2021 International Conference on Model and Data Engineering (MEDI’2021) held remotely in Tallinn (Estonia) (Attiogbe and Ben Yahia 2021). This special issue unveils new trends in developing data-driven application systems that seek to adapt computational algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modeling. Original research and review work with models and building data-driven applications using computational algorithms were particularly sought after. This special issue, aiming to provide state-of-the-art information to academics, researchers, and industry practitioners on Advances in Data-driven Engineering, attracted a total of eleven (11) submissions, five (5) of which had their initial versions among the sixteen (16) full papers presented during the MEDI’2021 conference. The remaining articles are contributions submitted in response to the general call for the special issue. Among the eleven submitted papers, the following six (6) papers were accepted after a thorough two-level reviewing process. The first paper in this special issue is authored by Garcia-Garcia et al. (2023). The authors introduced the design and the implementation of efficient distributed algorithms for distance join queries in Spark-based spatial analytics systems. They look into how to make and use efficient distance-based join queries and distributed algorithms in Apache Sedona. The authors improved the new in-memory cluster computing system for processing large-scale spatial data using the best spatial partitioning and other optimization techniques. In the second paper, Ellouze,Mechtib, and Belguith (2023) proposed a supervised learning method leveraging multimodal information for paranoid detection in French tweets