Researchers at Purdue University Propose GTX: A Transactional Graph Data System for HTAP Workloads

Researchers from Purdue University have introduced GTX to address the challenge of handling large-scale graphs with high throughput read-write transactions while maintaining competitive graph analytics. Managing dynamic graphs efficiently is crucial for various applications like fraud detection, recommendation systems, and graph neural network training. Real-world graphs often exhibit temporal localities and hotspots, which existing transactional […]

The post Researchers at Purdue University Propose GTX: A Transactional Graph Data System for HTAP Workloads appeared first on MarkTechPost.

ai shorts analytics applications challenge data detection dynamic editors pick fraud fraud detection graph graph analytics graph data graph neural network graphs htap network network training neural network purdue university recommendation recommendation systems researchers scale staff systems tech news technology training transactions university while workloads world