TEFD: A Benchmark for Natural Language to Flux Query Generation in Time-Series Databases
TL;DR
This work introduces the Text-to-Flux task and proposes FluxEngine, a novel automated pipeline for dataset construction, and provides the essential infrastructure to foster future research in NLI for TSDBs.
Abstract
The proliferation of IoT and real-time monitoring has established Time-Series Databases (TSDBs) like InfluxDB as critical infrastructure. However, their functional query languages (e.g., Flux) present a steep learning curve, hindering data accessibility for non-experts. While Natural Language Interfaces (NLIs) offer a potential solution, the domain of Text-to-Flux is stalled by a critical bottleneck: the complete absence of diverse, high-quality paired benchmarks. To address this, we introduce the Text-to-Flux task and propose FluxEngine, a novel automated pipeline for dataset construction. Unlike static generation methods used in Text-to-SQL, our framework features a Self-Sustaining Live Data Context that utilizes background tasks to perpetually generate fresh data, ensuring that queries involving relative time windows (e.g., ''past hour'') remain executable and valid indefinitely. Using this framework, we construct and release TEFD (Text-to-Flux Dataset), the first large-scale benchmark for this task. We further define execution-based evaluation metrics tailored for time-series validity. This work provides the essential infrastructure to foster future research in NLI for TSDBs. To facilitate reproducibility and future research, our dataset and benchmark code are publicly available at https://github.com/gta886/TEFD-Benchmark.