Data preparation is the process of transforming raw data into a clean and consumable format. It is widely known as the bottleneck to extract value and insights from data, due to the number of possible tasks in the pipeline and factors that can largely affect the results, such as human expertise, application scenarios, and solution methodology. Researchers and practitioners devised a great variety of techniques and tools over the decades, while many of them still place a significant burden on human's side to configure the suitable input rules and parameters. In this thesis, with the goal of reducing human manual effort, we explore using the power of statistical analysis techniques to automate three subtasks in the data preparation pipeline: data enrichment, error detection, and entity matching. Statistical analysis is the process of discovering underlying patterns and trends from data and deducing properties of an underlying distribution of probability from a sample, for example, testing hypotheses and deriving estimates. We first discuss CrawlEnrich, which automatically figures out the queries for data enrichment via web API data, by estimating the potential benefit of issuing a certain query. Then we study how to derive reusable error detection configuration rules from a web table corpus, so that end-users get results with no efforts. Finally, we introduce AutoML-EM, aiming to automate the entity matching model development process. Entity matching is to find the identical entities in real-world. Our work provides powerful angles to automate the process of various data preparation steps, and we conclude this thesis by discussing future directions.
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Thesis advisor: Wang, Jiannan
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