Industry executives and experts share their predictions for 2018. Read them in this 10th annual VMblog.com 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, Wish.com 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
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.