DataXFormer: A robust transformation discovery system

Ziawasch Abedjan, John Morcos, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

58 Citations (Scopus)

Abstract

In data integration, data curation, and other data analysis tasks, users spend a considerable amount of time converting data from one representation to another. For example US dates to European dates or airport codes to city names. In a previous vision paper, we presented the initial design of DataXFormer, a system that uses web resources to assist in transformation discovery. Specifically, DataXFormer discovers possible transformations from web tables and web forms and involves human feedback where appropriate. In this paper, we present the full fledged system along with several extensions. In particular, we present algorithms to find (i) transformations that entail multiple columns of input data, (ii) indirect transformations that are compositions of other transformations, (iii) transformations that are not functions but rather relationships, and (iv) transformations from a knowledge base of public data. We report on experiments with a collection of 120 transformation tasks, and show our enhanced system automatically covers 101 of them by using openly available resources.

Original languageEnglish
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1134-1145
Number of pages12
ISBN (Electronic)9781509020195
DOIs
Publication statusPublished - 22 Jun 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: 16 May 201620 May 2016

Publication series

Name2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016

Conference

Conference32nd IEEE International Conference on Data Engineering, ICDE 2016
Country/TerritoryFinland
CityHelsinki
Period16/05/1620/05/16

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