The 11th IEEE International Conference on Smart Data (SmartData 2025) will be held in Zhengzhou, Henan, China, October 30- November 02, 2025. Smart data aims to filter out the noise data and produce valuable data, which can be effectively used by enterprises and governments for planning, operation, monitoring, control, and intelligent decision making. Although unprecedentedly large amount of sensory data can be collected with the advancement of the Cyber-Physical-Social systems, the key is to explore how big data can become smart data and offer intelligence. Advanced big data modeling and analytics are indispensable for discovering the underlying structure from retrieved data and further acquiring smart data.

The IEEE SmartData 2025 is to promote community-wide discussion identifying the computational intelligence technologies and theories for harvesting smart data from big data. It will provide a high-profile, leading-edge forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences and work-in-process on all aspects of smart data. Topics for submissions include but are not limited to the following theory, technology, and applications.

IEEE SmartData 2025 Tracks and Topics

Track 1: Data Science and lts Foundations

    Foundational Theories for Data Science
    Data Classification and Taxonomy
    Data Metrics and Metrology
    Data inference for Smart/Big Data
    Theoretical Models for Smart/Big Data

Track 2: Smart/Big Data Infrastructure and Systems

    Cloud/Cluster/Fog/Edge Computing
    Parallel Computing for Big Data
    Open Source Big Data Systems
    System Architecture and Infrastructure
    Smart/Big Data Appliance

Track 3: Smart/Big Data Storage and Management

    Data Collection, Transformation and Transmission
    Data Integration, Cleaning and Storage
    Data Query and indexing Technologies
    Distributed File/Database Systems
    NewSQL/NoSQL for Smart/Big Data

Track 4: Smart/Big Data Processing and Analytics

    Smart/Big Data Search, Mining, Drilling
    Machine Learning/Deep Learning
    Brain/Nature-Inspired Computing
    Secure/Privacy-Preserving/Differentially Private Computing
    Visualization Analytics for Smart/Big Data

Track 5: Smart/Big Data Applications

    Parallel Computing for Big Data
    Smart/Big Data Applications in All Fields
    Data as a Service (DaaS)
    Smart/Big Data Opening, Sharing, and Trading
    Ethic lssues in Big/Smart Data

Track 6: Smart/Big Data in Internet of Things (loT)

    Network Design, Scheduling and Optimization
    Age of Information for Data Collection and Analytics in loT
    Learning-based Decision-making with Smart/Big Data in loT
    Mobile Data Collection, Transmission and Analytics in loT
    New Models, Algorithms and Methods for Smart City