Abstract
Pareto dominance plays an important role in diverse application domains such as economics and e-commerce, and it is widely being used in multicriteria decision making. In these cases, objectives are usually contradictory and therefore it is not straightforward to provide a set of items that are the “best” according to the user’s preferences. Skyline queries have been extensively used to recommend the most dominant items. However, in some cases skyline items are either too few, or too many, causing problems in selecting the prevailing ones. The number of skyline items depend heavily on both the data distribution, the data population and the dimensionality of the data set. In this work, we provide a dominance-based analysis and querying scheme that aims at alleviating the skyline cardinality problem, trying to introduce ranking on the items. The proposed scheme can be used either as a mining or as a querying tool, helping the user in selecting the mostly preferred items. Performance evaluation based on different distributions, populations and dimensionalities show the effectiveness of the proposed scheme.
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Papadopoulos, A.N., Lyritsis, A., Nanopoulos, A., Manolopoulos, Y. (2007). Domination Mining and Querying. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_14
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DOI: https://doi.org/10.1007/978-3-540-74553-2_14
Publisher Name: Springer, Berlin, Heidelberg
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