# LearnPADS + + : Incremental Inference of Ad Hoc Data Formats

## Author(s): Zhu, Kenny Q; Fisher, Kathleen; Walker, David

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DC FieldValueLanguage
dc.contributor.authorZhu, Kenny Q-
dc.contributor.authorFisher, Kathleen-
dc.contributor.authorWalker, David-
dc.date.accessioned2021-10-08T19:47:52Z-
dc.date.available2021-10-08T19:47:52Z-
dc.date.issued2012en_US
dc.identifier.citationZhu, Kenny Q., Kathleen Fisher, and David Walker. "LearnPADS + + : Incremental Inference of Ad Hoc Data Formats." In International Symposium on Practical Aspects of Declarative Languages, pp. 168-182. doi:10.1007/978-3-642-27694-1_13en_US
dc.identifier.issn0302-9743-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1d834-
dc.description.abstractAn ad hoc data source is any semi-structured, non-standard data source. The format of such data sources is often evolving and frequently lacking documentation. Consequently, off-the-shelf tools for processing such data often do not exist, forcing analysts to develop their own tools, a costly and time-consuming process. In this paper, we present an incremental algorithm that automatically infers the format of large-scale data sources. From the resulting format descriptions, we can generate a suite of data processing tools automatically. The system can handle large-scale or streaming data sources whose formats evolve over time. Furthermore, it allows analysts to modify inferred descriptions as desired and incorporates those changes in future revisions.en_US
dc.format.extent168 - 182en_US
dc.language.isoen_USen_US
dc.relation.ispartofInternational Symposium on Practical Aspects of Declarative Languagesen_US
dc.rightsAuthor's manuscripten_US