Data wrangling is a critical yet often labor-intensive process, essential for transforming raw data into formats suitable for downstream tasks such as machine learning or data analysis. Traditional data wrangling methods can be time-consuming, resource-intensive, and prone to errors, limiting the efficiency and effectiveness of subsequent downstream tasks. In this paper, we introduce AutoDW: an end-to-end solution for automatic data wrangling that leverages the power of Large Language Models (LLMs) to enhance automation and intelligence in data preparation. AutoDW distinguishes itself through several innovative features, including comprehensive automation that minimizes human intervention, the integration of LLMs to enable advanced data processing capabilities, and the generation of source code for the entire wrangling process, ensuring transparency and reproducibility. These advancements position AuoDW as a superior alternative to existing data wrangling tools, offering significant improvements in efficiency, accuracy, and flexibility. Through detailed performance evaluations, we demonstrate the effectiveness of AutoDW for data wrangling. We also discuss our experience and lessons learned from the industrial deployment of AutoDW, showcasing its potential to transform the landscape of automated data preparation.

In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. A crucial part of our algorithm is the incremental maintenance of the occurrences of possibly frequent patterns using a tree sweeping technique. We give modifications of the algorithm to other online mining model. We present theoretical and empirical analyses to evaluate the performance of the algorithm.

1 Introduction By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML data [23] has been available on intra and internet. These electronic data are heterogeneous collection of ill-structured data that have no rigid structures, and often called semi-structured data [1]. Hence, there have been increasing demands for automatic methods for extracting useful information, particularly, for discovering rules or patterns from large collections of semi-structured data, namely, semi-structured data mining [6, 11, 18, 19, 21, 25].