Data Science for Service

Co-track chairs

Carlos Flavián

University of Zaragoza

Spain

Eleonora Pantano

University of Bristol

United Kingdom

Sijun Wang

Loyola Marymount University

USA

Indicative Topics

Scalable data processing architectures for real-time service analytics
Knowledge extraction from unstructured service data
Predictive analytics models for proactive service decision-making
Deep learning approaches for pattern recognition in service usage data
Data fusion techniques for integrating heterogeneous service datasets
Personalization of services through user behavior data mining
Privacy-preserving service data analytics
Sentiment and opinion mining for customer-centered service improvement
Service-specific big data models
Text-mining for AI-powered conversational agents and virtual assistants in service contexts
Generative AI for service innovation
Reinforcement learning for adaptive service management

Conference Tracks