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Authors: Hana Alouaoui ; Lotfi Lakhal ; Rosine Cicchetti and Alain Casali

Affiliation: Laboratoire d’Informatique et Système, CRNS UMR 7020, Aix Marseille Université and France

Keyword(s): Databases, IR, MCDA, Ranking, Skylines.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Foundations of Knowledge Discovery in Databases ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Structured Data Analysis and Statistical Methods ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: Discovering Skylines in Databases have been actively studied to effectively identify optimal tuples/objects with respect to a set of designated preference attributes. Few approaches have been proposed for ranking the skylines to resolve the problem of the high cardinality of the result set. The most recent approach to rank skylines is the dp-idp (dominance power-inverse dominance power) which extensively uses the Pareto-dominance relation to determine the score of each skyline. The dp-idp method is in the very same spirit as tf-idf weighting scheme from Information Retrieval. In this paper, we firstly make an Enrichment of dp-idp with Dominance Hierarchy to facilitate the determination of Skyline scores, we propose then the CoSky method (Cosine Skylines) for fast ranking skylines in Databases without computing the Pareto-dominance relation. Cosky is a TOPSIS-like method (Technique for Order of Preference by Similarity to Ideal Solution) resulting from the cross-fertilization between the fields of Information Retrieval, Multiple Criteria Decision Analysis, and Databases. The innovative features of CoSky are principally: the automatic weighting of the normalized attributes based on Gini index, the score of each skyline using the Saltons cosine of the angle between each skyline object and the ideal object, and its direct implementation into any RDBMS without further structures. Finally, we propose the algorithm DeepSky, a Multilevel skyline algorithm based on CoSky method to find Top-k ranked Skylines. (More)

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Paper citation in several formats:
Alouaoui, H.; Lakhal, L.; Cicchetti, R. and Casali, A. (2019). CoSky: A Practical Method for Ranking Skylines in Databases. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 508-515. DOI: 10.5220/0008363005080515

@conference{kdir19,
author={Hana Alouaoui. and Lotfi Lakhal. and Rosine Cicchetti. and Alain Casali.},
title={CoSky: A Practical Method for Ranking Skylines in Databases},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR},
year={2019},
pages={508-515},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008363005080515},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR
TI - CoSky: A Practical Method for Ranking Skylines in Databases
SN - 978-989-758-382-7
IS - 2184-3228
AU - Alouaoui, H.
AU - Lakhal, L.
AU - Cicchetti, R.
AU - Casali, A.
PY - 2019
SP - 508
EP - 515
DO - 10.5220/0008363005080515
PB - SciTePress