TigerGraph Brings Together Pattern Matching And Efficient Graph Computation

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According to a recent press release, “TigerGraph, the only scalable graph database for the enterprise, today introduced its latest release, TigerGraph 2.4. The new technology combines 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. The new release makes it easier than ever for enterprises to use deep computational analytics to gain insights from data. Pattern matching has been around for a long time, but business insights from the technique have been constrained by two problems: difficulty in scaling the computational requirements for large datasets and an inability to do deep link analytics, which requires going more than three hops or levels deep into the dataset.”

The release goes on, “For example, determining ultimate beneficiary ownership in banking and financial services means traversing from each subsidiary to its parent business unit all the way up to the corporate headquarters, looking up the key stakeholders for each organization and adding up the ownership portions for each stakeholder across the corporate structure. With every hop, the size of data in the search expands exponentially, requiring massively parallel computation to traverse the data. Each new hop opens up a new world of information, but competing graph databases have only been able to scratch the surface because of their inability to handle these increasingly complex computations. AI and ML developers, for example, have long sought deeper analysis of interconnected data. The deeper the insights, the better the patterns and corresponding features, which leads to more accurate outcomes for business initiatives.”

Read more at tigergraph.com.

Image used under license from Shutterstock.com

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