TwinDB: Interactive What-If Analysis for Digital Twins

Digital twins enable what-if analysis by simulating physical systems under hypothetical conditions, but simulation is computationally expensive. In this context, a scenario represents a conf iguration of input parameters (e.g., wall insulation, window properties, heating setpoints) defining a hypothetical condition to evaluate. A building energy analyst exploring retrofit options for 500 buildings faces hours of waiting time for a typical session. Wedemonstrate TwinDB, a scenario-aware database system that achieves significant speedup through Incremental Scenario Maintenance (ISM), which incrementally updates cached simulation results when parameters change slightly rather than re-running full simulations. Our interactive visual workspace lets conference attendees (1) explore retrofit scenarios with instant feedback, (2) compare scenarios side-by-side with confidence intervals, (3) verify ISM accuracy against full simulation, (4) write declarative TwinQL queries (a domain-specific language extending SQL for scenario-based analytics), and (5) upload custom CSV data. Built on PostgreSQL with TimescaleDB and fully open-source, TwinDB transforms digital twin analytics from batch processing to interactive exploration.