TigerGraph, creator of the world’s first and only native parallel graph database platform for enterprise applications, announced its presence at Graph Day Texas, taking place Jan. 27 in Austin, TX. The company will be sponsoring and speaking at the event.
Dr. Victor Lee, director of product management at TigerGraph, will deliver an overview of the company’s distributed Native Parallel Graph and its deployment as a fraud detection system that manages hundreds of billions or more graph elements to detect risk and fraudulent groups in real-‐time. He will also discuss the techniques behind the distributed Native Parallel Graph platform, including how it partitions graph data across machines, supports fast update, and the ability to perform fast graph traversal and computation. In addition, he’ll share a subsecond real-‐time fraud detection system managing 100 billion graph elements to detect risk and fraudulent groups.
In September 2017, TigerGraph announced its emergence from stealth with $31M in Series A funding, the general availability of the TigerGraph technology and availability of both its Cloud Service and GraphStudio, TigerGraph’s visual software development kit (SDK). TigerGraph’s Native Parallel Graph Technology (NPG) powers real-‐time deep link analytics for enterprises with complex and colossal amounts of data. The technology is already in use by leading customers including Alipay, VISA, SoftBank, State Grid Corporation of China and Wish.
TigerGraph is the world’s fastest graph analytics platform designed to unleash the power of interconnected data for deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph supports applications such as IoT, AI and machine learning to make sense of ever-changing big data. TigerGraph’s proven technology is used by customers including Uber, VISA, Alipay, Wish, China Mobile, State Grid Corporation of China, and Zillow.