Recognizing the promise of natural language interfaces to databases, prior
studies have emphasized the development of text-to-SQL systems. While
substantial progress has been made in this field, existing research has
concentrated on generating SQL statements from text queries. The broader
challenge, however, lies in inferring new information about the returned data.
Our research makes two major contributions to address this gap. First, we
introduce a novel Internet-of-Things (IoT) text-to-SQL dataset comprising
10,985 text-SQL pairs and 239,398 rows of network traffic activity. The dataset
contains additional query types limited in prior text-to-SQL datasets, notably
temporal-related queries. Our dataset is sourced from a smart building's IoT
ecosystem exploring sensor read and network traffic data. Second, our dataset
allows two-stage processing, where the returned data (network traffic) from a
generated SQL can be categorized as malicious or not. Our results show that
joint training to query and infer information about the data can improve
overall text-to-SQL performance, nearly matching substantially larger models.
We also show that current large language models (e.g., GPT3.5) struggle to
infer new information about returned data, thus our dataset provides a novel
test bed for integrating complex domain-specific reasoning into LLMs.

基于物联网文本到 SQL 数据集，我们研究了如何从返回的数据中推断新信息，通过两阶段处理（查询和数据信息推断）可以改善文本到 SQL 的性能，并提供了测试领域特定推理的新方法。