Introduction

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 2026 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.

Topics

Topics for submissions include but not limited to the following:

Track 1: Data Governance, Ethics & Foundations

    AI and Data Ethics
    Data Privacy & Security in the Age of LLMs
    Data Provenance, Trust & Lineage
    Fairness, Accountability & Transparency (FAT) in AI
    Modern Data Governance Strategies & Frameworks
    Data Quality, Cleaning & Integration
    Distributed & Decentralized Data Systems
    Blockchain for Data Management

Track 2: Smart Data Infrastructure & Systems

    Cloud, Edge & Fog Computing for Smart Data
    High-Performance & Parallel Computing for Big Data
    Open Source Big Data Ecosystems (Spark, Flink, etc.)
    System Architectures for Smart Data Pipelines
    Energy-Efficient & Green Data Processing
    6G-Enabled Data Infrastructure
    Serverless & Microservices Architectures
    Data Streaming & Real-Time Processing

Track 3: Machine Learning & AI Algorithms

    Core Machine Learning & Deep Learning Algorithms
    Generative AI & Large Language Models (LLMs)
    Reinforcement Learning & Decision Making
    Federated & Collaborative Learning
    Transfer Learning & Domain Adaptation
    Explainable AI (XAI) & Interpretable Models
    Graph Neural Networks & Geometric Deep Learning
    Nature-Inspired & Evolutionary Computing
    Foundation Models & Multimodal AI

Track 4: AI-driven Analytics & Cybersecurity

    Secure & Privacy-Preserving Analytics
    AI for Cybersecurity & Threat Intelligence
    Social Media, Web & Behavioral Analytics
    Graph Analytics & Network Science
    Anomaly Detection & Intrusion Detection Systems
    LLM-based Security Analysis & Vulnerability Detection

Track 5: Smart Data Applications

    Smart Data Applications in All Fields (Health, Finance, etc.)
    Data as a Service (DaaS) & Data Marketplaces
    Smart City & Urban Computing
    Autonomous Systems & Self-Driving Vehicles
    Crowdsourcing & Human-in-the-Loop Systems
    Digital Twins & Simulation-Driven Data
    Ethical & Societal Impact of Smart Data Applications

Track 6: Smart Data in Internet of Things (IoT)

    Network Design, Scheduling & Optimization for IoT
    Age of Information for Data Collection & Analytics in IoT
    Learning-Based Decision-Making with Smart Data in IoT
    Mobile Data Collection, Transmission & Analytics in IoT
    TinyML & Edge AI for Resource-Constrained IoT Devices
    Security & Privacy in IoT Data Ecosystems
    IoT Data Protocols & Standards (MQTT, CoAP, Matter)
    Ambient Intelligence & Smart Environment Sensing