NousDB engine greatly speeds semantic data queries using new optimizations
NousDB - A Fast Query Engine for Semantic Analytics
Databases
Summary
Semantic analytics means using AI to understand and analyze complex data that isn't just numbers or text but includes meaning and context. Current systems are slow because they analyze data piece by piece and have limited ways to improve speed. The authors present NousDB, a fast query engine that adds special operations and smart planning to handle semantic data more efficiently. Their methods allow queries to run up to 86 times faster while maintaining good accuracy. This helps mix traditional data processing with newer AI-powered insights in one system.
What this means in practice
- •For enterprise data teams: Process complex data queries mixing structured and unstructured data faster by integrating semantic AI operators at scale within existing pipelines.
- •For software engineers building ai platforms: Build AI-infused query engines that efficiently batch model calls and optimize semantic operations to speed up natural language data analysis.
Authors
Christos Tsapelas, Georgia Koutrika, Yannis Ioannidis
Abstract
Semantic data analytics systems integrate AI-powered operators into query pipelines, enabling declarative queries over structured and unstructured data. However, existing systems rely on per-tuple model invocations, ad hoc operator designs, and lack principled cost models, resulting in poor scalability and limited optimization. We present NousDB, a high-performance semantic analytics engine built on Apache DataFusion\cite{10.1145/3626246.3653368}.Through NousDB we propose an extended relational algebra with three semantic operators and formal algebraic properties that enable principled query rewriting and cross-operator optimization. We further propose a token-based cost model using equi-depth token histograms to optimize batch sizing under context-window constraints and provide statistical guarantees for safe batching. Experiments show that NousDB achieves up to $\times 86$ speedups with both local and enterprise LLMs, while leading competitors in token efficiency and accuracy. These gains extend to complex JOB and Stack workloads augmented with semantic operators, supporting both traditional and semantic analytics.