Editorial on the Special Issue of the World Wide Web journal with selected papers from the 22nd International Conference on Web Information Systems Engineering (WISE)

World Wide Web (2024) 27:45 https://doi.org/10.1007/s11280-024-01284-1 Editorial on the Special Issue of the World Wide Web journal with selected papers from the 22nd International Conference on Web Information Systems Engineering (WISE) Richard Chbeir1 · Helen Huang2 · Yannis Manolopoulos3 · Fabrizio Silvestri4 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024 The 22nd International Conference on Web Information Systems Engineering (WISE) took place at Biarritz/France during the period 1–3 of November 2022. The Call for Papers of the conference included, non-exclusively, the following topics: ● ● ● ● ● ● ● ● ● ● Information Retrieval and Recommendation for Web. Web Mining and Knowledge Discovery Web Data Models. Social Issues and Analysis on Web. Distributed and Cloud Computing for Web Information Systems. Trustworthy and Responsible Web Information Systems. Web Applications for Economy, Society, Health, Human Being and Things. Extension of the Web. Rich Web UI and HCI. Web Tools and Visualization. Web Open-sourced Datasets. WISE-2022 conference attracted a significant number of papers, submitted from nearly all continents, out of which 47 papers were presented at the conference. Out of these 47 papers, 13 papers were selected as candidates for a special issue in the World Wide Web journal. Finally, 9 papers journal submissions were accepted after a thorough review process of drastically extended versions of the conference papers. In the sequel, this set of 9 papers is briefly presented. The first paper by Otoya Nakakaze, István Koren, Florian Brillowski and Ralf Klamma (RWTH Aachen) examines adaptive retrofitting for industrial machines. The authors con- Yannis Manolopoulos 1 Universit? de Pau et des Pays de l’Adour, Pau, France 2 University of Queensland, St Lucia, QLD, Australia 3 Open University of Cyprus, Latsia, Cyprus 4 Sapienza University of Rome, Rome, Italy 13 45 Page 2 of 5 World Wide Web (2024) 27:45 sider that leveraging previously untapped data sources offers significant potential for value creation in the manufacturing sector. However, asset-heavy shop floors, extended machine replacement cycles, and equipment diversity necessitate considerable investments for achieving smart manufacturing, which can be particularly challenging for small businesses. Retrofitting presents a viable solution, enabling the integration of low-cost sensors and microcontrollers with older machines to collect and transmit data. They introduce a prototype for retrofitting industrial environments using lightweight web technologies at the edge. Their approach employs WebAssembly as a novel bytecode standard, facilitating a consistent development environment from the cloud to the edge by operating on both browsers and bare-metal hardware. By attaining near-native performance and modularity reminiscent of container-based service architectures, they demonstrate the feasibility of their approach. Their prototype is evaluated with an actual industrial robot within a showcase factory, including measurements of data exchange with a cutting-edge data lake system. They further extend the prototype to incorporate a peer-to-peer network that facilitates message routing and WebAssembly software updates. Their technology establishes a foundational framework for the transition towards Industry 4.0. By integrating considerations of sustainability and human factors, it further extends this groundwork to facilitate progression into Industry 5.0. The second paper by Yong-Feng Ge, Hua Wang (Victoria University), Jinli Cao (La Trobe University), Yanchun Zhang (Peng Cheng Laboratory, Shenzhen) and Xiaohong Jiang (Future University Hakodate) focus in privacy-preserving data publishing. The privacy-preserving data publishing (PPDP) problem has gained substantial attention from research communities, industries, and governments due to the increasing requirements for data publishing and concerns about data privacy. However, achieving a balance between preserving privacy and maintaining data quality remains a challenging task in PPDP. The authors present an information-driven distributed genetic algorithm (ID-DGA) that aims to achieve optimal anonymization through attribute generalization and record suppression. The proposed algorithm incorporates various components, including an informationdriven crossover operator, an information-driven mutation operator, an information-driven improvement operator, and a two-dimensional selection operator. Furthermore, a distributed population model is utilized to improve population diversity while reducing the running time. Experimental results confirm the superiority of ID-DGA in terms of solution accuracy, convergence speed, and the effectiveness of all the proposed components. The third paper by Joris Knoester, Flavius Frasincar (Erasmus University of Rotterdam) and Maria Mihaela Truşcǎ (Bucharest University of Economic Studies) deals with sentiment analysis issues. Given the growth of Web and its popularity, Aspect-based Sentiment Analysis (ABSA) has been established as an important tool to understand people’s preferences. Despite the existence of big data, the lack of data annotations restricts the supervised ABSA analysis to only a limited number of domains. To this end a transfer learning strategy is implemented by extending the state-of-the-art LCR-Rot-hop + + model for ABSA with the methodology of Domain Adversarial Training (DAT). The output is a cross-domain deep learning structure, called DAT-LCR-Rot-hop++. Its major advantage is that it does not require any labeled target domain data. The results are obtained for six different domain combinations with testing accuracies ranging up to 74%. Once DAT-LCR-Rot-hop + + is able to find the similarities between domains, it produces good results. However, if the domains are too distant, it is not capable of generating domain-invariant features. This result 13 World Wide Web (2024) 27:45 Page 3 of 5 45 is amplified by adding the neutral aspects to the positive or negative class. The performance of DAT-LCR-Rot-hop + + is very dependent on the similarity between distributions of source and target domain and the presence of a dominant sentiment class in the training set. The fourth paper authored by Firas Zouari, Chirine Ghedira-Guegan (University of Lyon), Khouloud Boukadi (University of Sfax) and Nadia Kabachi (University of Lyon) examines data curation issues in data lakehouses. Data lakehouses are receiving much interest from industrial and academic fields due to their ability to hold disparate multi-structured batch and streaming data sources in a single data repository. Data heterogeneity and complexity issues require a dedicated process to improve their quality and extract added value. Therefore, with curation tasks, data are cleaned and enriched to ensure their fitting to user requirements. Nowadays, existing data curation t (...truncated)