Theme and Scope

Recently, web application paradigms have shifted fundamentally toward smart, browser-driven web architectures, where statistical learning and machine learning models are integrated directly into the web ecosystem and browser runtime environments. The rapid expansion of interactive web applications, real-time data streams, dynamic user tracking, and web-based multimodal interactions generates vast volumes of high-velocity data. Traditional static web architectures and server-dependent analytics struggle to process these complex, high-dimensional web interactions in real time while maintaining low latency and strict user privacy.

To address these challenges, modern web frameworks leverage client-side execution environments—such as WebGPU, WebAssembly, and W3C Web Neural Network APIs—alongside smart web microservices. By pairing browser-based local model execution with server-side foundational models, these environments enable seamless, real-time data analysis, personalized user experiences, and automated workflows directly within the user interaction loop. This paradigm supports instant dynamic UI adaptation, client-side privacy-preserving analytics, real-time sentiment and intent analysis, and collaborative web intelligence that conventional web systems cannot efficiently deliver.

However, designing data analysis pipelines and service architectures tailored for these browser-driven environments presents distinct research challenges. Key open issues include web-native model compression and runtime optimization, client-side data security, federated browser learning, dynamic workflow orchestration, real-time data streaming, and web application power consumption [1, 2].

To address these limitations and advance next-generation web technologies, active research on web-native data analysis, browser-based services, and intelligent web frameworks is progressing rapidly [3, 4]. This special issue invites original research, architectural designs, and demonstration studies in fields such as intelligent web platforms, tech-driven e-learning, real-time web analytics, web-based digital assistants, web security, and adaptive user interfaces within these modern web environments.

Potential Topics

Potential topics include, but are not limited to:

• Client-side model compression, quantization, and runtime optimization for web browsers
• Acceleration of browser-based data processing using WebGPU, WebAssembly, and W3C Web Neural Network APIs
• Hybrid web service architectures combining browser-local processing with server-side models
• Federated learning and privacy-preserving data analytics across web browsers
• Real-time client-side user sentiment, intent, and behavioral tracking
• On-device data streaming, caching, and state synchronization in modern web applications
• Instant dynamic UI/UX adaptation and personalized web interaction loops
• Web-based digital assistants and automated client-side workflow orchestration
• Resource and power consumption optimization for data-intensive web applications
• Intelligent web platforms for adaptive e-learning and collaborative environments
• Web security, client-side anomaly detection, and threat analysis in browser runtimes
• Real-time multimodal data fusion (text, voice, UI interactions) within web applications

Keywords: Browser-driven intelligence, client-side analytics, WebGPU and WebAssembly, in-browser machine learning, privacy-preserving web analytics, intelligent web microservices, real-time web workflows

Instructions for Manuscripts

• Submitted papers should not be currently under consideration for publication elsewhere.
• Previously published conference papers should be extended by at least 70% new material for the journal version.
• All papers are refereed through a peer review process.
• All submitted papers must conform to the layout and format guidelines of JIT.
• Submissions must be directly sent via email to Leading Guest Editor, Dr. Jeongeun Byun: jebyun@kisti.re.kr

Important Dates

• Submission Deadline: June 30, 2027
• Tentative Publication Date: The 1st quarter, 2029

Guest Editors

• Dr. Jeongeun Byun (Leading Guest Editor)
• Director of Technology Commercialization Research Center, Korea Institute of Science and Technology Information (KISTI), Korea
• Email: jebyun@kisti.re.kr

• Prof. Jason C. Hung
• Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taiwan
• Email: jhung@gm.nutc.edu.tw

• Prof. Haejong Joo
• Head of Department of Computer Engineering at the Catholic Kwandong University, Korea
• Email: hjjoo007@cku.ac.kr

References

[1] R. Jahangir, D. Ishii, The Accessibility Capability Boundary: Operational Limits and Expansion Potential of AI-Generated Browser-Native Accessibility Systems, ArXiv preprint, arXiv:2605.19638, May, 2026. https://doi.org/10.48550/arXiv.2605.19638

[2] A. K. Alnaim, K. M. Albarrak, A Privacy-Preserving Federated Learning Framework for Web User Behavior over Fog Infrastructure, Systems, Vol. 14, No. 4, Article No. 442, April, 2026. https://doi.org/10.3390/systems14040442

[3] S. M. Thahir, S. K. G., J. A. S., Bridging BPMN and Edge-IoT: A Reactor-based architecture for real-time, AI-driven workflow orchestration, Journal of Systems Architecture, Vol. 176, Article No. 103807, July, 2026. https://doi.org/10.1016/j.sysarc.2026.103807

[4] I. Gligorea, M. Cioca, R. Oancea, A.-T. Gorski, H. Gorski, P. Tudorache, Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review, Education Sciences, Vol. 13, No. 12, Article No. 1216, December, 2023. https://doi.org/10.3390/educsci13121216