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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Towards Automatic Data Format Transformations: Data Wrangling at Scale
verfasst von : Alex Bogatu, Norman W. Paton, Alvaro A. A. Fernandes
Erschienen in: Data Analytics
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Abstract
Data wrangling is the process whereby data is cleaned and integrated for analysis. Data wrangling, even with tool support, is typically a labour intensive process. One aspect of data wrangling involves carrying out format transformations on attribute values, for example so that names or phone numbers are represented consistently. Recent research has developed techniques for synthesising format transformation programs from examples of the source and target representations. This is valuable, but still requires a user to provide suitable examples, something that may be challenging in applications in which there are huge data sets or numerous data sources. In this paper we investigate the automatic discovery of examples that can be used to synthesise format transformation programs. In particular, we propose an approach to identifying candidate data examples and validating the transformations that are synthesised from them. The approach is evaluated empirically using data sets from open government data.