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Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To address these shortcomings, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions. 

Dataset discovery can be performed using search (with a query or keywords) to find relevant data. However, the result of this discovery can be overwhelming to explore. Existing navigation techniques mostly focus on linkage graphs that enable navigation from one data set to another based on similarity or joinability of attributes. However, users often do not know which data set to start the navigation from. RONIN proposes an alternative way to navigate by building a hierarchical structure on a collection of data sets: the user navigates between groups of data sets in a hierarchical manner to narrow down to the data of interest. We demonstrate RONIN, a tool that enables user exploration of a data lake by seamlessly integrating the two common modalities of discovery: data set search and navigation of a hierarchical structure. In RONIN, a user can perform a keyword search or joinability search over a data lake, then, navigate the result using a hierarchical structure, called an organization , that is created on the fly. While navigating an organization, the user may switch to the search mode, and back to navigation on an organization that is updated based on search. This integration of search and navigation provides great power in allowing users to find and explore interesting data in a data lake.