Scaling Vector Databases With Novel Partitioning Methodologies

This document analyzes the operational and scaling challenges of storing and querying billion-scale cybersecurity datasets in vector databases, explains how minor file modifications evade static hash-based detection and bloat datasets with near-duplicates, and proposes a novel linear-time representative partitioning and hierarchical cascade method to reduce storage and query costs while preserving diverse and meaningful coverage for threat detection and retrieval.