TigerGraph, a native parallel graph database-as-a-service, has released new improvements for security, advanced AI, and machine learning features to simplify adoption, implementation, and management.
It is common to employ graph databases for use cases like AI and machine learning, fraud detection, and recommendation engines since they are structured to uncover patterns and relationships between recorded data nodes within a range of data formats and systems. According to Gartner, graph technologies will make up 80% of data and analytics advances by 2025, up from 10% in 2021. Explainable AI is one use case for a graph database that is very relevant.
The platform from TigerGraph provides a variety of data and ML research tools that let people look inside the AI black box and determine the reasons behind certain decisions made by an algorithm.
“Graph is a crucial tool for solving business challenges and TigerGraph is committed to helping customers unlock the full potential of their data by using ML and AI to close the gap between data and decisions,” said Jay Yu, VP of product and innovation at TigerGraph in a statement.
TigerGraph is a native graph database, which means that rather than non-native graphs that were created for other purposes but have added graph features, it was created expressly to store and query connected data. According to TigerGraph, a native graph database is preferable for clients whose applications often query and make use of relationships among persons, goods, places, or other entities, or if the use case makes use of network effects or necessitates multiple-hop queries across data.