Deploying machine learning (ML) inference pipelines in databases become increasingly prevalent in many applications. In order to avoid data transfer between the database and ML runtimes, existing ML2SQL frameworks parse ML pipelines to a graph consisting of ML operators and then translate it into pure SQL. Nevertheless, they typically rewrite the graph without operator fusion or only consider the fusion between certain operators such as StandardScaler and tree inference. However, there are various operators in ML pipelines, which have rich fusion opportunities between each other. To fully exploit operator fusion for graph rewriting, we classify widely used ML operators and design fusion rules driven by their characteristics. Moreover, rewriting the original graph by fusion rules produces candidate graphs that generate SQLs with different execution time. We employ an enumeration-based strategy to search for the graph with the lowest cost. However, this strategy may suffer from the combination explosion on search space for complex ML pipelines. To reduce this space, we propose a greedy-based strategy by exploiting the independence among ML operators. We implement a novel ML2SQL framework as a portable plugin for databases, namely Craftsman. Our experimental evaluations show that, in comparison to the existing approaches, Craftsman generates efficient SQL queries which achieves an average speedup of 2.9x on popular databases such as DuckDB.

Exploratory data analysis (EDA), coupled with SQL, is essential for data analysts involved in data exploration and analysis. However, data analysts often encounter two primary challenges: (1) the need to craft SQL queries skillfully and (2) the requirement to generate suitable visualization types that enhance the interpretation of query results. Due to its significance, substantial research efforts have been made to explore different approaches to address these challenges, including leveraging large language models (LLMs). However, existing methods fail to meet real-world data exploration requirements primarily due to (1) complex database schema, (2) unclear user intent, (3) limited cross-domain generalization capability, and (4) insufficient end-to-end text-to-visualization capability. This paper presents TiInsight, an automated SQL-based cross-domain exploratory data analysis system. First, we propose a hierarchical data context (i.e., HDC), which leverages LLMs to summarize the contexts related to the database schema, which is crucial for open-world EDA systems to generalize across data domains. Second, the EDA system is divided into four components (i.e., stages): HDC generation, question clarification and decomposition, text-to-SQL generation (i.e., TiSQL), and data visualization (i.e., TiChart). Finally, we implemented an end-to-end EDA system with a user-friendly GUI in the production environment at PingCAP. We have also open-sourced all APIs of TiInsight to facilitate research within the EDA community. Through extensive evaluations by a real-world user study, we demonstrate that TiInsight offers remarkable performance compared to human experts. Additionally, TiSQL achieves an execution accuracy of 86.3% on the Spider dataset when using GPT-4. It also attains an execution accuracy of 60.98% on the Bird test dataset.