GenIE - Simulator-Driven Iterative Data Exploration for Scientific Discovery
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911267729440768 |
|---|---|
| author | Colaco, Ashwin Gerard Boissier, Martin Rao, Sriram Ghosh, Shubharoop Mehrotra, Sharad Rabl, Tilmann |
| author_facet | Colaco, Ashwin Gerard Boissier, Martin Rao, Sriram Ghosh, Shubharoop Mehrotra, Sharad Rabl, Tilmann |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenIE - Simulator-Driven Iterative Data Exploration for Scientific Discovery Colaco, Ashwin Gerard Boissier, Martin Rao, Sriram Ghosh, Shubharoop Mehrotra, Sharad Rabl, Tilmann Databases 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. |
| title | GenIE - Simulator-Driven Iterative Data Exploration for Scientific Discovery |
| topic | Databases |
| url | https://arxiv.org/abs/2511.12057 |