TigerGraph Offers Graph Pattern Matching in Latest Update

TigerGraph is releasing an updated version of its signature platform, combining graph pattern matching with real-time deep link analytics -- a unique mix ideal for fraud and money laundering detection, security analytics, personalized recommendation engines, artificial intelligence and machine learning.

TigerGraph 2.4. makes it easier than ever for enterprises to use deep computational analytics to gain insights from data, according to the company.

“Unlike other graph databases on the market that delve two to three level deep into the connected data, TigerGraph's pattern analytics is tuned to be efficient and tractable with the ability to go 10 or more levels deep into the interconnected entities and calculate risk or similarity scores based on multi-dimensional criteria in real-time,” said Dr. Yu Xu, CEO and founder, TigerGraph. “Efficient graph analytics is more than just a great massively parallel processing engine; it’s understanding what users want to know and focusing on that, and pruning away the rest.”

Standard pattern matching solutions have a defined starting point such as a specific customer account or payment and a well-defined pattern with a fixed number of hops such as traversal from a customer account to all the payments originating from the account to recipients of those payments etc.

Discovering fraud or money laundering loops is complex, as it does not have a defined starting point as the payment may originate from any customer account and it also does not have defined number of hops as fraudsters or money launderers often use 10+ layers of synthetic accounts to hide their activities.

With its massively parallel processing (MPP) engine, TigerGraph 2.4 addresses both the standard as well as complex pattern matching for datasets of all sizes.

TigerGraph’s GSQL pattern-matching support lets users express multi-hop queries in a compact, easy-to-read format. By expressing the multi-hop patterns in one line, the transparency of patterns used in analytics and feature engineering for machine learning is improved.

In addition to these updates, AWS users can use their S3 data natively in GraphStudio, further improving the efficiency of the AWS cloud business user.

GraphStudio has been praised for how easy it is to map data stored in local files into the graph schema, using a drag-and-drop GUI.

The same ease-of-use is now available for AWS users who have data in S3 files. Native S3 Import from GraphStudio offers better synergy with popular cloud data store and easy to use data import, making it simpler to run TigerGraph on AWS.

The company also announced the new TigerGraph JDBC Connector, making it easier than ever for Java developers to integrate TigerGraph into their applications.

For more information about these updates, visit