Data Engineering Lab's broad research areas are data mining and machine learning. Specifically, we works on graph (network) mining, text mining, and social network analysis. One aspect of our research is developing novel effective and efficient graph processing methods. Another aspect is to conduct biomedical knowledge mining to track public health issues in social networks. Moreover, we aim to discover significant, unprecedent knowledge from biomedical entities' interaction.
We are glad to inform that our research paper titled " Proximity-Based Compression for Network Embedding" has been accepted at Frontiers in Big data.
We are glad to inform that our research paper with title "Paper Recommendation Based on Citation Relation" has been accepted at REU 2019 Symposium at IEEE International Conference on Big Data.
Two research papers with the title "Index based Closest Community Search" and "Network Embedding: on Compression and Learning" have been accepted at 2019 IEEE International Conference on Big Data.