A METRIC FOR RANKING HIGH DIMENSIONAL SKYLINE QUERIES

Marlene Goncalves, Graciela Perera

Abstract

Skyline queries have been proposed to express user’s preferences. Since the size of Skyline set increases as the number of criteria augments, it is necessary to rank high dimensional Skyline queries. In this work, we propose a new metric to rank high dimensional Skylines which allows to identify the k most interesting objects from the Skyline set (Top-k Skyline). We have empirically studied the variability and performance of our metric. Our initial experimental results show that the metric is able to speed up the computation of the Top-k Skyline in up to two orders of magnitude w.r.t. the state-of-the-art metric: Skyline Frequency.

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Paper Citation


in Harvard Style

Goncalves M. and Perera G. (2010). A METRIC FOR RANKING HIGH DIMENSIONAL SKYLINE QUERIES . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-04-1, pages 383-386. DOI: 10.5220/0002904803830386


in Bibtex Style

@conference{iceis10,
author={Marlene Goncalves and Graciela Perera},
title={A METRIC FOR RANKING HIGH DIMENSIONAL SKYLINE QUERIES},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2010},
pages={383-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002904803830386},
isbn={978-989-8425-04-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A METRIC FOR RANKING HIGH DIMENSIONAL SKYLINE QUERIES
SN - 978-989-8425-04-1
AU - Goncalves M.
AU - Perera G.
PY - 2010
SP - 383
EP - 386
DO - 10.5220/0002904803830386