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Title: Optimization of Skyline queries in dynamic contexts

Citation Type: Miscellaneous

Publication Year: 2020

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Abstract: Preference queries are interesting tools to compute small representatives of datasets. In this thesis, we mainly focus on the optimization of Skyline queries in dynamic contexts. In a first part, we address the incremental maintenance of the multidimensional indexing structure NSC which has been shown efficient for answering skyline queries in a static context. More precisely, we address (i) the case of dynamic data, i.e. tuples are inserted or deleted at any time, and (ii) the case of streaming data, i.e. tuples are appended and discarded at specific interval of time. In a second part, we address the optimization of skyline queries in presence of dynamic orders, i.e, some or all attributes of the dataset are nominal and each user expresses his/her own partial order on these attributes’ domain. In that case, we propose scalable parallel algorithms that decompose an issued query into a set of sub-queries and process each sub-query independently.

Url: https://tel.archives-ouvertes.fr/tel-03043999/document

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Authors: Alami, Karim

Publisher: HAL

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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