🤖 AI Summary
This paper addresses key challenges in cross-source entity relationship modeling—namely, the difficulty of automatically identifying explicit/implicit dependencies, excessive manual intervention, and weak support for multilingual and heterogeneous environments. To this end, we propose the first end-to-end, data-stream-oriented entity relationship modeling framework. Our method integrates dynamic sampling, robust data analysis, SQL parsing, and natural language interfaces to automatically discover database constraints and construct both explicit and implicit dependency models, supporting real-time processing and visualization across major SQL dialects. Key contributions include: (i) the first data-stream-driven relational modeling approach that jointly optimizes semantic understanding and system efficiency; and (ii) multi-backend (CPU/GPU) and multilingual deployment capability. Evaluated on the STATS benchmark, our framework achieves 2.4× higher distributional representation efficiency, 2.6× faster constraint learning speed, 2.15× greater inference throughput, 1.19× improved data narrative accuracy, and 1.86× reduced contextual resource consumption.
📝 Abstract
Exploring relationships across data sources is a crucial optimization for entities recognition. Since databases can store big amount of information with synthetic and organic data, serving all quantity of objects correctly is an important task to deal with. However, the decision of how to construct entity relationship model is associated with human factor. In this paper, we present flow-oriented entity-relationship tool. This is first and unique end-to-end solution that eliminates routine and resource-intensive problems of processing, creating and visualizing both of explicit and implicit dependencies for prominent SQL dialects on-the-fly. Once launched, FLOWER automatically detects built-in constraints and starting to create own correct and necessary one using dynamic sampling and robust data analysis techniques. This approach applies to improve entity-relationship model and data storytelling to better understand the foundation of data and get unseen insights from DB sources using SQL or natural language. Evaluated on state-of-the-art STATS benchmark, experiments show that FLOWER is superior to reservoir sampling by 2.4x for distribution representation and 2.6x for constraint learning with 2.15x acceleration. For data storytelling, our tool archives 1.19x for accuracy enhance with 1.86x context decrease compare to LLM. Presented tool is also support 23 languages and compatible with both of CPU and GPU. Those results show that FLOWER can manage with real-world data a way better to ensure with quality, scalability and applicability for different use-cases.