Machine Learning Applied to Detecting Fraud in Healthcare
Fraud is a major contributing factor to escalating healthcare costs: fraud costs the U.S. about $60 billion annually and accounts for up to 10 percent of total healthcare spending. Is…
Just a Key-Value Problem? How Graph Reduces Memory Consumption, Accelerates Performance and Overcomes Problems with Key-Value Databases
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…
The Beauty of Graph Algorithms with Built-in Parallelism
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),…
Cognitive Computing, Associative Memory and Graph
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…
Graph-Based Customer Entity Resolution
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…
On “Benchmarking RedisGraph 1.0”
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…
Building a Graph Database on a Key-Value Store?
by Dr. Xu Yu, CEO and Dr. Victor Lee, Director of Product Management [Excerpted from the eBook Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link…