Big Data

Strategic Research Area

The mission of VRAC focuses on three major areas.

  • Analytics
  • Visualization
  • Machine learning, expert systems, and high-fidelity

VRAC’s interdisciplinary approach to big data research combines expertise in computer science, data science, engineering, visualization, and machine learning. Our work is not only advancing the theoretical understanding of these fields but also contributing to practical applications in areas such as scientific research, industry, and healthcare.

Example research: 


  • Big Data Processing: VRAC researchers have been working on scalable and efficient techniques for processing and analyzing massive datasets. This involves developing algorithms and frameworks for distributed data processing to extract valuable insights from large-scale data sources.

  • Predictive Analytics: VRAC is exploring predictive modeling techniques using big data. This involves developing algorithms and models to forecast future trends, make data-driven decisions, and optimize various processes in fields ranging from agriculture to healthcare.


  • Immersive Data Visualization: VRAC specializes in immersive and interactive data visualization. Researchers are using Virtual Reality (VR) and Augmented Reality (AR) technologies to create immersive data environments that allow users to explore complex datasets in three-dimensional space.

  • Visual Analytics: Combining the power of data analysis and visualization, VRAC is at the forefront of developing visual analytics tools. These tools enable users to interactively explore data, identify patterns, and gain insights more effectively.

Machine Learning:

  • Expert Systems: VRAC researchers are developing expert systems that leverage machine learning and big data to mimic human expertise in various domains. These systems are designed to make informed decisions, provide recommendations, and solve complex problems in fields such as agriculture, engineering, and healthcare.

  • High-Fidelity Approximations: In machine learning, VRAC is researching techniques for creating high-fidelity approximations of complex processes. This involves training models to accurately replicate real-world phenomena, enabling simulations and predictions with a high degree of accuracy.