Learning Transformation Rules by Examples
暂无分享,去创建一个
This paper presents an abstract for a general data transformation approach. Using programming by demonstration technique, we learn the transformation rules through user given examples. These transformation rules are automatically generated from a predefined grammar. Due to the grammar space is huge, we propose a grammar space reduction method to reduce the search space and a sketch of search algorithm is adopted to identify the rules that are consistent with the examples. The final experimental results show our approach achieves promising results on different transformation scenarios.
[1] Jeffrey Heer,et al. Wrangler: interactive visual specification of data transformation scripts , 2011, CHI.
[2] Michael I. Jordan,et al. Learning Programs: A Hierarchical Bayesian Approach , 2010, ICML.
[3] Hong Ma. Google Refine – http://code.google.com/p/google-refine/ , 2012 .
[4] Joseph M. Hellerstein,et al. Potter's Wheel: An Interactive Data Cleaning System , 2001, VLDB.
[5] Csaba Szepesvári,et al. Bandit Based Monte-Carlo Planning , 2006, ECML.