A common question from the TigerGraph prospects who know Cypher and want to learn GSQL is “Do you have an example of the movie database from Neo4j?”. So, I thought it would be interesting to share an implementation of that movie database in GSQL as...
Two recent, related partnerships between highly specialised “graph” database developers and public cloud platform providers underscores the importance of specialised databases capable of surfacing insights that would otherwise remain hidden within traditional database architectures. Read the full article here....
Why is graph technology such a step forward? Virtually all existing risk assessment and monitoring systems are built on traditional relational databases, which store information such as counterparty, account, transaction, stakeholders, financial instruments and derivatives in separate tables, one for each type of business entity. Read...
As criminals deploy complex strategies and modern technology for tax evasion, graph databases can be used effectively by the IRS and other agencies all over the world to catch the crooks. Byline by VP of Marketing Read the full article here....
Neo4j's loading time is shorter than TigerGraph; however, Neo4j requires extra preprocessing that extracts the vertex file from the edge file. After including the pre-processing time, Neo4j takes longer time to loading than TigerGraph. TigerGraph can effectively compresses the data size and needs 19.3x less storage space than Neo4j's. On...
The risk of money laundering spans the entire financial services ecosystem - banks, payment providers and newer cryptocurrencies, such as Bitcoin and Ripple and more. Given how much financial activity occurs every second, everyday, it’s important for banks and financial organizations to develop a robust...
CEO Yu Xu explains how graph analytics has emerged at the forefront as an ideal technology to support Anti-Money Laundering (AML). Graphs overcome the challenge of uncovering the relationships in massive, complex and interconnect data. The graph model is designed from the ground up to...
Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both performance and analysis capabilities—when the volume of data grows very large, and when the answers must be provided in real time. That’s because existing graph...
