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2018 | OriginalPaper | Buchkapitel

Non-index Based Skyline Analysis on High Dimensional Data with Uncertain Dimensions

verfasst von : Nurul Husna Mohd Saad, Hamidah Ibrahim, Fatimah Sidi, Razali Yaakob

Erschienen in: Databases and Information Systems

Verlag: Springer International Publishing

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Abstract

The notion of skyline query is to find a set of objects that is not dominated by any other objects. Regrettably, existing works lack on how to conduct skyline queries on high dimensional uncertain data with objects represented as continuous ranges and exact values, which in this paper is referred to as uncertain dimensions. Hence, in this paper we define skyline queries over data with uncertain dimensions and propose an algorithm, SkyQUD, to efficiently answer skyline queries. The SkyQUD algorithm determines skyline objects through three methods that guaranteed the probability of each object being in the final skyline results: exact domination, range domination, and uncertain domination. The algorithm has been validated through extensive experiments employing real and synthetic datasets. Results exhibit our proposed algorithm is efficient and scalable in answering skyline query on high dimensional and large datasets with uncertain dimensions.

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Vorheriges Kapitel A New Knowledge-Transmission Based Horizontal Collaborative Fuzzy Clustering Algorithm for Unequal-Length Time Series

Nächstes Kapitel Semi-automatic Quasi-morphological Word Segmentation for Neural Machine Translation

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Titel: Non-index Based Skyline Analysis on High Dimensional Data with Uncertain Dimensions
verfasst von: Nurul Husna Mohd Saad
Hamidah Ibrahim
Fatimah Sidi
Razali Yaakob
Verlag: Springer International Publishing
Buch: Databases and Information Systems
Print ISBN: 978-3-319-97570-2

Electronic ISBN: 978-3-319-97571-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-97571-9_22

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