@inproceedings{au2016query-biased,
author = {Au, Vincent and Thomas, Paul and Jayasinghe, Gaya K},
title = {Query-biased summaries for tabular data},
booktitle = {Proceedings of the Australasian Document Computing Symposium},
year = {2016},
month = {December},
abstract = {Government, research, and academic data portals publish a large amount of public data, but present tools make discovery difficult. In particular, search results do not support a user's decision whether or not to commit to a download of what might be a large data set.

We describe a method for producing query-biased summaries of tabular data, which aims to support a user's download decision—or even to answer the question on the spot, with no further interaction. The method infers simple types in the data and query; automatically refines queries, where that makes sense; extracts relevant subsets of the complete table; and generates both graphical and tabular summaries of what remains. A small-scale user study suggests this both helps users identify useful results (fewer false negatives), and reduces wasted downloads (fewer false positives).},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/query-biased-summaries-tabular-data/},
edition = {Proceedings of the Australasian Document Computing Symposium},
}