Abstract:Data science is essential for the extraction of value from data. However, the most tedious part of the process, data wrangling, implies a range of mostly manual formatting, identification and cleansing manipulations. Data wrangling still resists automation partly because the problem strongly depends on domain information, which becomes a bottleneck for state-of-the-art systems as the diversity of domains, formats and structures of the data increases. In this thesis we focus on generating algorithms that take a… Show more
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