research-article

DataXFormer: An Interactive Data Transformation Tool

Authors:

Paolo Papotti, and

Michael StonebrakerAuthors Info & Claims

SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

May 2015

Pages 883 - 888

https://doi.org/10.1145/2723372.2735366

Published: 27 May 2015 Publication History

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Abstract

While syntactic transformations require the application of a formula on the input values, such as unit conversion or date format conversions, semantic transformations, such as "zip code to city", require a look-up in some reference data. We recently presented DataXFormer, a system that leverages Web tables, Web forms, and expert sourcing to cover a wide range of transformations. In this demonstration, we present the user-interaction with DataXFormer and show scenarios on how it can be used to transform data and explore the effectiveness and efficiency of several approaches for transformation discovery, leveraging about 112 million tables and online sources.

References

[1]

Z. Abedjan, J. Morcos, M. Gubanov, I. Ilyas, M. Stonebraker, P. Papotti, and M. Ouzzani. Dataxformer: Leveraging the web for semantic data transformations. In CIDR, 2015.

Google Scholar

[2]

B. Aditya, G. Bhalotia, S. Chakrabarti, A. Hulgeri, C. Nakhe, P. Parag, and S. Sudarshan. Banks: Browsing and keyword searching in relational databases. In VLDB, pages 1083--1086, 2002.

Digital Library

Google Scholar

[3]

S. Agrawal, S. Chaudhuri, and G. Das. Dbxplorer: A system for keyword-based search over relational databases. In ICDE, pages 5--16, 2002.

Digital Library

Google Scholar

[4]

L. Barbosa and J. Freire. An adaptive crawler for locating hidden-web entry points. In WWW, pages 441--450, New York, NY, USA, 2007.

Digital Library

Google Scholar

[5]

A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the em algorithm. Applied statistics, pages 20--28, 1979.

Google Scholar

[6]

V. Hristidis and Y. Papakonstantinou. Discover: Keyword search in relational databases. In VLDB, pages 670--681, 2002.

Digital Library

Google Scholar

[7]

S. Kandel, A. Paepcke, J. Hellerstein, and J. Heer. Wrangler: Interactive visual specification of data transformation scripts. In CHI, pages 3363--3372, New York, NY, USA, 2011.

Digital Library

Google Scholar

[8]

J. Madhavan, D. Ko, L. Kot, V. Ganapathy, A. Rasmussen, and A. Halevy. Google's deep web crawl. PVLDB, 1(2):1241--1252, Aug. 2008.

Digital Library

Google Scholar

[9]

M. Stonebraker, D. Bruckner, I. F. Ilyas, G. Beskales, M. Cherniack, S. B. Zdonik, A. Pagan, and S. Xu. Data curation at scale: The data tamer system. In CIDR, 2013.

Google Scholar

[10]

M. Yakout, K. Ganjam, K. Chakrabarti, and S. Chaudhuri. Infogather: Entity augmentation and attribute discovery by holistic matching with web tables. In SIGMOD, pages 97--108, New York, NY, USA, 2012.

Digital Library

Google Scholar

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Xiong KFu SDing GLuo ZYu RChen WBao HWu Y(2023)Visualizing the Scripts of Data Wrangling With SomnusIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.314497529:6(2950-2964)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TVCG.2022.3144975
Xiong KLuo ZFu SWang YXu MWu Y(2022)Revealing the Semantics of Data Wrangling Scripts With COMANTICSIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209470(1-11)Online publication date: 2022
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Kundu S(2022)The Fourth Paradigm in Geographical SciencesLivelihood Enhancement Through Agriculture, Tourism and Health10.1007/978-981-16-7310-8_25(495-507)Online publication date: 18-Jan-2022
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Index Terms

DataXFormer: An Interactive Data Transformation Tool
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems
2. Information systems
  1. Data management systems

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Information

Published In

SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

May 2015

2110 pages

ISBN:9781450327589

DOI:10.1145/2723372

General Chair:
Timos Sellis
RMIT University, Australia
,
Program Chairs:
Susan B. Davidson
University of Pennsylvania, USA
,
Zack Ives
University of Pennsylvania, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2015

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SIGMOD/PODS'15

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SIGMOD

SIGMOD/PODS'15: International Conference on Management of Data

May 31 - June 4, 2015

Victoria, Melbourne, Australia

Acceptance Rates

SIGMOD '15 Paper Acceptance Rate 106 of 415 submissions, 26%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Xiong KFu SDing GLuo ZYu RChen WBao HWu Y(2023)Visualizing the Scripts of Data Wrangling With SomnusIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.314497529:6(2950-2964)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TVCG.2022.3144975
Xiong KLuo ZFu SWang YXu MWu Y(2022)Revealing the Semantics of Data Wrangling Scripts With COMANTICSIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209470(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209470
Kundu S(2022)The Fourth Paradigm in Geographical SciencesLivelihood Enhancement Through Agriculture, Tourism and Health10.1007/978-981-16-7310-8_25(495-507)Online publication date: 18-Jan-2022
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Xia SAnzum NSalihoglu SZhao JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)KTabulator: Interactive Ad hoc Table Creation using Knowledge GraphsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445227(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445227
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