LearnPADS: Automatic tool generation from ad hoc data

Kathleen Fisher, David Walker, Kenny Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

In this demonstration, we will present LearnPADS, a fully automatic system for generating ad hoc data processing tools. When presented with a collection of ad hoc data, the system (1) analyzes the data, (2) infers a pads [4, 5] description, (3) generates parser, printer, validation and traversal libraries and (4) links these libraries with format-independent tool suites to form stand-alone applications. These applications provide statistical analysis, XML conversion, CSV conversion, the ability to query with the Galax XQuery engine [3], and the ability to graph selected data elements, all directly from ASCII ad hoc data without human intervention. SIGMOD attendees will see both the user experience with LEARNPADS and the internals of the multi-phase inference algorithm which lies at the heart of the system.

Original languageEnglish (US)
Title of host publicationSIGMOD 2008
Subtitle of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data 2008
Pages1299-1301
Number of pages3
DOIs
StatePublished - Dec 10 2008
Event2008 ACM SIGMOD International Conference on Management of Data 2008, SIGMOD'08 - Vancouver, BC, Canada
Duration: Jun 9 2008Jun 12 2008

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

Other2008 ACM SIGMOD International Conference on Management of Data 2008, SIGMOD'08
CountryCanada
CityVancouver, BC
Period6/9/086/12/08

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • Algorithms
  • Languages

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  • Cite this

    Fisher, K., Walker, D., & Zhu, K. (2008). LearnPADS: Automatic tool generation from ad hoc data. In SIGMOD 2008: Proceedings of the ACM SIGMOD International Conference on Management of Data 2008 (pp. 1299-1301). [1376759] (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/1376616.1376759