GenIE - Simulator-Driven Iterative Data Exploration for Scientific Discovery

2025-11-15

AI Summary

In scientific discovery and risk assessment, the decoupling of physics-based simulators (e.g., WRF-SFIRE, HYSPLIT) from databases causes high interactive latency and low exploration efficiency. Method: This paper proposes “database-aware simulation”—a novel paradigm that deeply embeds multi-hazard simulation models into the PostgreSQL extension architecture, enabling query-driven, dynamic simulation scheduling. The system supports on-demand invocation, reuse of intermediate results, iterative refinement of simulation accuracy, and a precision–latency trade-off mechanism. Contribution/Results: Experiments demonstrate that the approach shifts wildfire and hurricane analysis from static pre-simulation to real-time, interactive exploration. While preserving critical accuracy, it reduces average latency by one to two orders of magnitude, significantly enhancing responsiveness and analytical efficiency in scientific exploration.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Physics-based simulators play a critical role in scientific discovery and risk assessment, enabling what-if analyses for events like wildfires and hurricanes. Today, databases treat these simulators as external pre-processing steps. Analysts must manually run a simulation, export the results, and load them into a database before analysis can begin. This linear workflow is inefficient, incurs high latency, and hinders interactive exploration, especially when the analysis itself dictates the need for new or refined simulation data. We envision a new database paradigm, entitled GenIE, that seamlessly integrates multiple simulators into databases to enable dynamic orchestration of simulation workflows. By making the database "simulation-aware," GenIE can dynamically invoke simulators with appropriate parameters based on the user's query and analytical needs. This tight integration allows GenIE to avoid generating data irrelevant to the analysis, reuse previously generated data, and support iterative, incremental analysis where results are progressively refined at interactive speeds. We present our vision for GenIE, designed as an extension to PostgreSQL, and demonstrate its potential benefits through comprehensive use cases: wildfire smoke dispersion analysis using WRF-SFIRE and HYSPLIT, and hurricane hazard assessment integrating wind, surge, and flood models. Our preliminary experiments show how GenIE can transform these slow, static analyses into interactive explorations by intelligently managing the trade-off between simulation accuracy and runtime across multiple integrated simulators. We conclude by highlighting the challenges and opportunities ahead in realizing the full vision of GenIE as a cornerstone for next-generation scientific data analysis.

Problem

• Manual simulation workflows require separate execution before database analysis
• Linear simulation processes cause high latency and hinder interactive exploration
• Databases treat simulators as external steps rather than integrated components

Innovation

• Integrates simulators into databases for dynamic workflow orchestration
• Dynamically invokes simulators based on user queries and analytical needs
• Manages trade-offs between simulation accuracy and runtime interactively