Towards searching amongst tables

Paul Thomas; Rollin Omari; Tom Rowlands

doi:10.1145/2838931.2838941

Towards searching amongst tables

Paul Thomas, Rollin Omari, Tom Rowlands

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

An increasing number of data sets are being published online, in institutional or government repositories as well as by individual researchers, journalists, and others. These data are often represented as tables of various kinds: however, repositories have poor search over and inside tables. It is difficult for a user to tell from a repository's portal whether a useful dataset is available, and this problem is only likely to get worse. We describe this problem, and demonstrate that the naïve approach of full-text search is not appropriate. We describe an alternative, based on inferring types of data and indexing columns as a unit, and demonstrate some improvements in early success especially when long captions are not available.

Original language	English
Title of host publication	ADCS 2015 - Proceedings of the 20th Australasian Document Computing Symposium
Editors	Sarvnaz Karimi, Laurence A. F. Park
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450340403
DOIs	https://doi.org/10.1145/2838931.2838941
Publication status	Published - 8 Dec 2015
Event	20th Australasian Document Computing Symposium, ADCS 2015 - Parramatta, Australia Duration: 8 Dec 2015 → 9 Dec 2015

Publication series

Name	ACM International Conference Proceeding Series
Volume	08-09-Dec-2015

Conference

Conference	20th Australasian Document Computing Symposium, ADCS 2015
Country/Territory	Australia
City	Parramatta
Period	8/12/15 → 9/12/15

Access to Document

10.1145/2838931.2838941

Cite this

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title = "Towards searching amongst tables",

abstract = "An increasing number of data sets are being published online, in institutional or government repositories as well as by individual researchers, journalists, and others. These data are often represented as tables of various kinds: however, repositories have poor search over and inside tables. It is difficult for a user to tell from a repository's portal whether a useful dataset is available, and this problem is only likely to get worse. We describe this problem, and demonstrate that the na{\"i}ve approach of full-text search is not appropriate. We describe an alternative, based on inferring types of data and indexing columns as a unit, and demonstrate some improvements in early success especially when long captions are not available.",

author = "Paul Thomas and Rollin Omari and Tom Rowlands",

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booktitle = "ADCS 2015 - Proceedings of the 20th Australasian Document Computing Symposium",

note = "20th Australasian Document Computing Symposium, ADCS 2015 ; Conference date: 08-12-2015 Through 09-12-2015",

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Thomas, P, Omari, R & Rowlands, T 2015, Towards searching amongst tables. in S Karimi & LAF Park (eds), ADCS 2015 - Proceedings of the 20th Australasian Document Computing Symposium., a8, ACM International Conference Proceeding Series, vol. 08-09-Dec-2015, Association for Computing Machinery, 20th Australasian Document Computing Symposium, ADCS 2015, Parramatta, Australia, 8/12/15. https://doi.org/10.1145/2838931.2838941

Towards searching amongst tables. / Thomas, Paul; Omari, Rollin; Rowlands, Tom.
ADCS 2015 - Proceedings of the 20th Australasian Document Computing Symposium. ed. / Sarvnaz Karimi; Laurence A. F. Park. Association for Computing Machinery, 2015. a8 (ACM International Conference Proceeding Series; Vol. 08-09-Dec-2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - An increasing number of data sets are being published online, in institutional or government repositories as well as by individual researchers, journalists, and others. These data are often represented as tables of various kinds: however, repositories have poor search over and inside tables. It is difficult for a user to tell from a repository's portal whether a useful dataset is available, and this problem is only likely to get worse. We describe this problem, and demonstrate that the naïve approach of full-text search is not appropriate. We describe an alternative, based on inferring types of data and indexing columns as a unit, and demonstrate some improvements in early success especially when long captions are not available.

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Towards searching amongst tables

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