Dynamic Duo: Quine & Novelty Detector for Insider Threats
In a big update to the VAST Advanced Persistent Threat blog, we demonstrate an end-to-end Quine plus Novelty Detector workflow.
Blog
Articles on streaming graph, real-time analytics, and anomaly detection from the thatDot team.
idFrom(): the simple function that’s key to Quine streaming graph
idFrom() seems like a simple function for generating node IDs but it is the key to Quine's ability to find complex patterns in high volume event streams.
Using Indicators of Behavior (IoB) Analysis for IoT data
Indicator of Behavior (IoB) analysis is extending beyond the cybersecurity domain to offer new value for finance, ecommerce, and especially IoT use cases.
Quine’s Real-time Temporal Event Sequencing Produces New Insights
Quine's standing queries, idFrom + deterministic labelling can be use to efficiently create any subgraph you need (e.g. sequence based) in real time. This makes alerts more timely and root cause analysis more efficient.
Graph Neural Networks for Quine
Quine 1.5 includes support for graph neural network techniques like Node2Vec and GraphSAGE. This post provides an overview and tutorial.
Categorical Data: An Untapped Source of Real-Time Insights
Categorical data is an oft-ignored source of valuable business intelligence. Quine makes it easy to process categorical data with your existing ETL pipeline.
Why Digital Twins Need to Go Real Time
For digital twins to be truly useful in business operations, they must be able to drive actions and do so the instant an issue emerges, perhaps even beforehand.
What is the difference between batch and stream processing?
Stream processing offers the opportunity to detect important patterns in information and act in real time.
Scaling Quine Streaming Graph to Process 1 Million Events/Second
Learn how Quine achieves groundbreaking performance for real-time complex event processing and how you can reproduce the results.
Streaming Graph ETL: Real-time Video Observability Simplified
Real-time video observability presents a number of data engineering challenges that graph ETL can solve.
Are You Ready for Low and Slow Auth Attacks?
Quine streaming graph detects hard to find password spraying attacks for IAM providers and enterprises alike.
What's the difference between Categorical and Numerical Data?
Categorical data is enormously useful but often discarded because, unlike numerical data, there were few tools available to work with it until graph DBs and streaming graph came along.