A skyline query processing approach over interval uncertain data stream with K-means clustering technique
Skyline query processing which extracts a set of interesting objects from a potentially large multidimensional dataset has attracted significant research attention in many emerging important applications. Although skyline computation has been studied extensively for data streams, there has been rela...
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| Main Authors: | , , , , , |
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| Format: | Proceeding Paper |
| Language: | en |
| Published: |
ThinkMind
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/80766/7/80766%20A%20Skyline%20Query%20Processing%20Approach.pdf http://irep.iium.edu.my/80766/ https://www.thinkmind.org/index.php?view=article&articleid=dbkda_2019_4_10_58001 |
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| Summary: | Skyline query processing which extracts a set of interesting objects from a potentially large multidimensional dataset has attracted significant research attention in many emerging important applications. Although skyline computation has been studied extensively for data streams, there has been relatively less work on uncertain data stream. Only recently, a few methods have been proposed to process uncertain data stream, however data uncertainty in these works is restricted to objects having many instances. In contrast, there is no work that has considered uncertainty due to objects having interval values wherein the exact values of the objects are not known at the point of processing. Hence, in this paper a skyline query processing approach utilising the K-Means clustering technique is proposed to efficiently compute skyline over interval uncertain data stream. |
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