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The big data analytics research in IMCL focuses on the Multi-source Cross-domain Big Data Fusion and Big Data Analytics for Multi-stage Complex System. Our mission is to academically and practically contributes with real values in smart logistics, smart health, smart education, and smart food safety:

  • Multi-source Cross-domain Big Data Fusion: Because of the dynamics, uncertainty, heterogeneity, and complexity of big data, fusing the multi-source and cross-domain data to gain useful and interpretable information as much as possible is a ground challenge in big data analytics. We develop novel data-driven approaches for effectively fusing multi-source and cross-domain big data at both feature level and model level.
  • Big Data Analytics for Multi-stage Complex System: Traditional big data analytics treats the system under study as an atomic entity with all its data directly available for analysis. However, most systems under study are complex, consisting of separable but interdependent subsystems, which are difficult to handle by existing single-stage methods. We develop new methodologies for multi-stage big data analytics on complex systems that address the challenges brought up by the diverse requirements, high dependency, and correlated objectives of the subsystems.


Multi-stage Big Data Analysis on Complex Systems

Tackling Grand Challenges in Food Safety: A Big Data and IoT Enabled Approach

Big Data-driven Airport Resource Optimization and Management

Data-driven On-demand Service Dispatching

Diagnosis Report Auto-Generation

Multi-stage Deep Learning for Early Predicting Severe COVID-19 Patients

Learning Analytics based on Multilayer Behavior Fusion