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TigerGraph 2018 Predictions: An Exciting Year for Big Graphs and Deep Link Analytics

VMblog Predictions 2018

Industry executives and experts share their predictions for 2018.  Read them in this 10th annual series exclusive.

Contributed by Yu Xu, CEO and founder of TigerGraph

2018: An Exciting Year for Big Graphs and Deep Link Analytics

The past year has been a big one for the big data and analytics market. Major events included IPOs from Cloudera and MongoDB, further validating the market. And, we're seeing enterprises continuing to recognize the fact that traditional solutions, such as relational databases (RDBMS), cannot meet all current needs in modern enterprise data management.

This offers just one reason why the graph data market is red hot and growing. Graphs overcome the challenge of storing massive, complex and interconnected data by storing data in a format that includes nodes, edges and properties. They offer several advantages over traditional RDBMS and newer big data products, including better suitability for relationship analysis to support real-time analytics.

In 2018, I believe we will see increased adoption of big data analytics in the cloud, along with continued investment and efforts by cloud vendors in offering solutions. Here are additional predictions for the market in the year ahead:

1.    Real-Time Big Graphs Will Become the Norm in the Modern Enterprise
We will continue to see adoption of real-time big graphs by companies with colossal amounts of data. Real-time big graphs incorporate hundreds of billions to trillions of graph elements (vertices or edges, equivalently entities or relationships). Today, real-time big graphs are already in use by some of the world's leading organizations, including Alipay, VISA, SoftBank, State Grid Corporation of China, and Elementum.

Representing the next stage in the graph database evolution, real-time big graphs are designed to deal with massive data volumes and data creation rates and to provide real-time analytics. Enterprises no longer need to struggle with slow data loading or slow query performance, and can reap insights into their big data for a unified view into their businesses.

2.    Deep Link Analytics Will Become the New BI

Enterprises demand real-time graph analytic capabilities that can explore, discover and predict very complex relationships. This represents deep link analytics, which is achieved utilizing three to 10+ hops of traversal across a big graph, along with fast graph traversal speed and data updates.

The result is real-time analytics for a range of enterprise applications including anti-money laundering, real-time fraud prevention, supply-chain logistics optimization, influence/risk score computation and more. We expect to see greater adoption of deep link analytics for these use cases and others in the year ahead as deep link analytics becomes the new BI.

3.    Deep Learning Will Improve Security and Fraud Detection 

To help combat security, next year enterprises need to make real-time decision engines, which take the freshest data in a graph-structured way, a top priority. Coupled with deep learning AI techniques, real-time decision engines will vastly improve the sensitivity of anti-fraud systems to detect phone and email scams, and other non-obvious attacks or anomalies.

Deep learning is effective for classifying objects that have correlated features. It's currently being used successfully to classify and interpret both image and voice data, where the object itself is a rich source of digital data. It is harder to classify some other types of entities, because their direct surface features don't seem to correlate.   However, with the assistance of a graph structure on each object's connections and relationships to other entities, deep learning will be the defining factor for increasing real-time fraud detection.

Technologies leveraging graph databases will power more and more enterprise AI, machine learning, cyber security and IoT applications. Indeed, the graph space continues to grow as TigerGraph emerged in September with the industry's first Native Parallel Graph technology, and Amazon just announced a limited preview of Amazon Neptune graph cloud offering.

I'm excited by these developments and look forward to seeing what's next for our market in 2018!


About the Author

yu xu 

Dr. Yu Xu is the founder and CEO of TigerGraph, the world's first native parallel graph database. Dr. Xu received his Ph.D. in Computer Science and Engineering from the University of California San Diego. He is an expert in big data and parallel database systems and has 26 patents in parallel data management and optimization. Prior to founding TigerGraph, Dr. Xu worked on Twitter's data infrastructure for massive data analytics. Before that, he worked as Teradata's Hadoop architect where he led the company's big data initiatives.

Published Friday, December 29, 2017 7:29 AM by David Marshall
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