AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic Analysis

TL;DR

AnDB is an AI-native database that unifies semantic analysis across structured and unstructured data, enabling intuitive AI-driven queries without requiring AI expertise, and optimizing execution for diverse data types.

Contribution

The paper introduces AnDB, a novel AI-native database that seamlessly integrates semantic analysis and query optimization for all data types, bridging the gap between structured and unstructured data.

Findings

Supports both OLTP and AI-driven tasks

Automates query optimization with multiple execution plans

Enables semantic queries with SQL-like statements

Abstract

In this demonstration, we present AnDB, an AI-native database that supports traditional OLTP workloads and innovative AI-driven tasks, enabling unified semantic analysis across structured and unstructured data. While structured data analytics is mature, challenges remain in bridging the semantic gap between user queries and unstructured data. AnDB addresses these issues by leveraging cutting-edge AI-native technologies, allowing users to perform semantic queries using intuitive SQL-like statements without requiring AI expertise. This approach eliminates the ambiguity of traditional text-to-SQL systems and provides a seamless end-to-end optimization for analyzing all data types. AnDB automates query processing by generating multiple execution plans and selecting the optimal one through its optimizer, which balances accuracy, execution time, and financial cost based on user policies and internal optimizing mechanisms. AnDB future-proofs data management infrastructure, empowering users to effectively and efficiently harness the full potential of all kinds of data without starting from scratch.