Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis

2025-10-14

AI Summary

Current analytical systems suffer from severe fragmentation across databases, analytical libraries, and tuning services—leading to complex interactions, poor adaptability, suboptimal performance, and limited scalability. To address this, we propose an AI-driven unified analytical system featuring a novel four-layer architecture (“application–task–model–data”), with the task layer serving as the central hub for cross-component adaptive scheduling, shared reuse, and secure model updates. Key technical contributions include: a declarative task interface; operator-plan compilation optimization; versioned model storage; cache-aware reuse; adaptive indexing; and constraint-aware feature management—enabling end-to-end automation under dynamic optimization objectives and multi-source heterogeneous data integration. Experimental results demonstrate substantial improvements in analytical efficiency and response flexibility, achieving low latency, cost-effectiveness, strong scalability, and enhanced usability.

Technology Category

• Machine Learning: Scalability of ML Systems
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

A growing trend in modern data analysis is the integration of data management with learning, guided by accuracy, latency, and cost requirements. In practice, applications draw data of different formats from many sources. In the meanwhile, the objectives and budgets change over time. Existing systems handle these applications across databases, analysis libraries, and tuning services. Such fragmentation leads to complex user interaction, limited adaptability, suboptimal performance, and poor extensibility across components. To address these challenges, we present Aixel, a unified, adaptive, and extensible system for AI-powered data analysis. The system organizes work across four layers: application, task, model, and data. The task layer provides a declarative interface to capture user intent, which is parsed into an executable operator plan. An optimizer compiles and schedules this plan to meet specified goals in accuracy, latency, and cost. The task layer coordinates the execution of data and model operators, with built-in support for reuse and caching to improve efficiency. The model layer offers versioned storage for index, metadata, tensors, and model artifacts. It supports adaptive construction, task-aligned drift detection, and safe updates that reuse shared components. The data layer provides unified data management capabilities, including indexing, constraint-aware discovery, task-aligned selection, and comprehensive feature management. With the above designed layers, Aixel delivers a user friendly, adaptive, efficient, and extensible system.

Problem

• Unifying fragmented AI data analysis systems across components
• Adapting to changing objectives and budgets over time
• Improving user interaction, performance, and extensibility challenges

Innovation

• Unified system for AI-powered data analysis
• Declarative interface with executable operator plans
• Adaptive layered architecture with versioned storage