Key-value databases have long been used for a variety of applications to provide pre-computed results in real-time. There is, however, an increasing shift towards using Graph technology for real-time applications that illustrates how this game-changing approach is not only ideal for complex datasets and complex...
Many people already know that graph algorithms are the most efficient and sometimes the only solution for complex business use cases, such as clustering different groups of users (Community Detection), finding influential persons or entities (PageRank Algorithm), or predicting user behaviors for personalized recommendation (Label...
How to Build an Associative Memory Capability in 1 Hour Recently I came across a wonderful presentation about Cognitive Computing and Associative Memory. Intel Saffron is a product dedicated for predictive analytics based on the associative memory concept. In this article I’ll show how to use...
1. Problem Description Enterprises know the benefits of merging data from multiple sources, to build more detailed and more complete records about their customers, their products, their employees, etc. The sources may be different departments or computer systems within one enterprise, a combination of internal and...
Recently RedisGraph published a blog [1], comparing their performance to that of TigerGraph's, following the tests [2] in TigerGraph's benchmark report [3], which requires solid performance on 3-hop, 6-hop, and even 10-hop queries. Multi-hop queries on large data sets are the future of graph analytics. After...
What a difference a year makes. One year ago, we presented the public launch of TigerGraph 1.0, delivering the next stage in the evolution of graph database with the world’s fastest and most scalable graph platform. This came after five years in stealth developing state-of-the-art technology...
The time is ripe for an international standard graph query language. Industry vendors including Neo4j have called this out, and we at TigerGraph wholeheartedly agree. As graphs continue to see widespread adoption, we have certainly reached a tipping point for our industry. Still, it is...
Today we are proud to announce TigerGraph 2.0 with real time MultiGraph collaboration services - an industry first that supports upcoming GDPR requirements. The release is all part of TigerGraph’s commitment to innovation as we empower customers to derive even deeper meanings from connected data....
Today, criminals are using extremely sophisticated, ever-adapting tactics to bypass traditional anti-fraud solutions for money laundering. Any organization facilitating financial transactions – including non-bank money service businesses such as digital/mobile payment services, life insurers and retailers, to name a few – falls within the scope...
Graph databases are the fastest growing category in all of data management, according to DB-Engines.com. What makes graph databases so popular? Graphs overcome the big and complex data challenges that other databases cannot. With the popularity of graph databases driven by such benefits, there has...
