Dominance analyses reduction in skyline query processing over data stream with data mining technique
The database community has observed in the past two decades, the growth of research interest in skyline queries, which aim to report to users interesting objects—commonly known as skylines—based on their preferences. The identification of skyline objects becomes more challenging when skylines are to...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Association for Computing Machinery
2025
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/118083/ http://psasir.upm.edu.my/id/eprint/118083/1/118083.pdf http://psasir.upm.edu.my/id/eprint/118083/2/118083-cover.pdf |
| _version_ | 1848867425375748096 |
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| author | Ahmed Mohamud, Mudathir Ibrahim, Hamidah Sidi, Fatimah Mohd Rum, Siti Nurulain |
| author_facet | Ahmed Mohamud, Mudathir Ibrahim, Hamidah Sidi, Fatimah Mohd Rum, Siti Nurulain |
| author_sort | Ahmed Mohamud, Mudathir |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The database community has observed in the past two decades, the growth of research interest in skyline queries, which aim to report to users interesting objects—commonly known as skylines—based on their preferences. The identification of skyline objects becomes more challenging when skylines are to be identified from a collection of continuously generated input data streams. In this paper, we proposed the Dominance Analyses Reduction (DAR) framework, which aims at addressing the issues of redundant dominance analyses that arise while determining skylines over data stream. Dominance analyses are repeated for objects that are in the overlapped frames of two windows and for pairs of objects that later reappear in the stream. DAR employs the Apriori algorithm, one of the most prevalent data mining algorithms, to identify the frequently occurring dominance analyses. Instead of conducting the dominance analyses again, their results are stored and utilised in the subsequent derivation of skylines. The DAR framework has been validated through several experiments. Its results exhibit significant reduction in the number of pairwise comparisons at both object and dimension levels and execution time. |
| first_indexed | 2025-11-15T14:36:17Z |
| format | Conference or Workshop Item |
| id | upm-118083 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T14:36:17Z |
| publishDate | 2025 |
| publisher | Association for Computing Machinery |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1180832025-06-24T04:32:38Z http://psasir.upm.edu.my/id/eprint/118083/ Dominance analyses reduction in skyline query processing over data stream with data mining technique Ahmed Mohamud, Mudathir Ibrahim, Hamidah Sidi, Fatimah Mohd Rum, Siti Nurulain The database community has observed in the past two decades, the growth of research interest in skyline queries, which aim to report to users interesting objects—commonly known as skylines—based on their preferences. The identification of skyline objects becomes more challenging when skylines are to be identified from a collection of continuously generated input data streams. In this paper, we proposed the Dominance Analyses Reduction (DAR) framework, which aims at addressing the issues of redundant dominance analyses that arise while determining skylines over data stream. Dominance analyses are repeated for objects that are in the overlapped frames of two windows and for pairs of objects that later reappear in the stream. DAR employs the Apriori algorithm, one of the most prevalent data mining algorithms, to identify the frequently occurring dominance analyses. Instead of conducting the dominance analyses again, their results are stored and utilised in the subsequent derivation of skylines. The DAR framework has been validated through several experiments. Its results exhibit significant reduction in the number of pairwise comparisons at both object and dimension levels and execution time. Association for Computing Machinery 2025 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/118083/1/118083.pdf text en http://psasir.upm.edu.my/id/eprint/118083/2/118083-cover.pdf Ahmed Mohamud, Mudathir and Ibrahim, Hamidah and Sidi, Fatimah and Mohd Rum, Siti Nurulain (2025) Dominance analyses reduction in skyline query processing over data stream with data mining technique. In: The 14th International Conference on Information Communication and Management (ICICM 2024), 6-8 Nov. 2024, Paris, France. (pp. 29-35). https://dl.acm.org/doi/10.1145/3711609.3711614 Theory of computation → Theory and algorithms for applica-tion domains; Database theory; Data structures and algorithms for data management. 10.1145/3711609.3711614 |
| spellingShingle | Theory of computation → Theory and algorithms for applica-tion domains; Database theory; Data structures and algorithms for data management. Ahmed Mohamud, Mudathir Ibrahim, Hamidah Sidi, Fatimah Mohd Rum, Siti Nurulain Dominance analyses reduction in skyline query processing over data stream with data mining technique |
| title | Dominance analyses reduction in skyline query processing over data stream with data mining technique |
| title_full | Dominance analyses reduction in skyline query processing over data stream with data mining technique |
| title_fullStr | Dominance analyses reduction in skyline query processing over data stream with data mining technique |
| title_full_unstemmed | Dominance analyses reduction in skyline query processing over data stream with data mining technique |
| title_short | Dominance analyses reduction in skyline query processing over data stream with data mining technique |
| title_sort | dominance analyses reduction in skyline query processing over data stream with data mining technique |
| topic | Theory of computation → Theory and algorithms for applica-tion domains; Database theory; Data structures and algorithms for data management. |
| url | http://psasir.upm.edu.my/id/eprint/118083/ http://psasir.upm.edu.my/id/eprint/118083/ http://psasir.upm.edu.my/id/eprint/118083/ http://psasir.upm.edu.my/id/eprint/118083/1/118083.pdf http://psasir.upm.edu.my/id/eprint/118083/2/118083-cover.pdf |