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16.05 - High Dimensional Data Reduction, Sampling and Visualization for Big Data Applications

Project - Summary


In this project, we aim to overcome the limitation and shortcoming of current visualization, data analysis, data sampling techniques to make sense of complex big data through latent theme extraction, to detect emerging practices, recommendation, and collaborative filtering.


  • We Develop a unifying platform for data dimensional reduction, data sampling and visualizing various complex high dimensional big data, Develop analytical and visualization tools and solutions for various real-world applications
  • Worked with IAB members to evaluate the prototype systems in a real-world setting.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Xiaohua Tony Hu PI Not available Not available Drexel University
Xiaoli Song Graduate Student Not available Not available Drexel University

Project - Impact and Uses/Benefits

We evaluated the proposed robust dimensional reduction, sampling and visualization with high level of performance in purpose of generating useful knowledge in big data applications on social media. However, our model can be applied to a wide variety of problems.

Project - Deep Dive

Project - Documents

FilenameFilesizeLast modified
16.05_year_5_final_report.pdf1.3 MiB2019/08/22 10:47
projects/year5/16.05.txt · Last modified: 2019/08/22 10:48 by sally.johnson