Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network
In moving environment, the positions of moving objects cannot be located accurately. Apart from the measuring instrument errors, movement of the objects is the main factor contributing to this uncertainty. This uncertainty makes dominant relationship of data instable, which will affect skyline opera...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/365064 |
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| _version_ | 1832559179150131200 |
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| author | Shanliang Pan Yihong Dong Jinfeng Cao Ken Chen |
| author_facet | Shanliang Pan Yihong Dong Jinfeng Cao Ken Chen |
| author_sort | Shanliang Pan |
| collection | DOAJ |
| description | In moving environment, the positions of moving objects cannot be located accurately. Apart from the measuring instrument errors, movement of the objects is the main factor contributing to this uncertainty. This uncertainty makes dominant relationship of data instable, which will affect skyline operator. In this paper, we mainly study the continuous probabilistic skyline query for uncertain moving objects in road network. The query point is deemed to be stationary while moving objects are treated as targets with uncertainty described by a probability density function. After defining the notion of dominant probability and probabilistic skyline, we put forward a novel algorithm to deal with continuous probabilistic skyline query on road network. Firstly, we compute the dominant probability and skyline probability to get initial permanent p -skyline set. Then we define events to predict the time when dominant relationship between moving objects may change. Furthermore, we track and calculate events to update the probabilistic skyline in an incremental way. Two pruning strategies are proposed to cancel invalid events and objects in a bid to diminish search space. Finally, an extensive experimental evaluation on real datasets shows that probabilistic skyline sets in road network can be updated by the proposed algorithm. It demonstrates both efficiency and effectiveness. |
| format | Article |
| id | doaj-art-e952d5423bef496c91cc161b75711d43 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-e952d5423bef496c91cc161b75711d432025-02-03T01:30:42ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-03-011010.1155/2014/365064365064Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road NetworkShanliang PanYihong DongJinfeng CaoKen ChenIn moving environment, the positions of moving objects cannot be located accurately. Apart from the measuring instrument errors, movement of the objects is the main factor contributing to this uncertainty. This uncertainty makes dominant relationship of data instable, which will affect skyline operator. In this paper, we mainly study the continuous probabilistic skyline query for uncertain moving objects in road network. The query point is deemed to be stationary while moving objects are treated as targets with uncertainty described by a probability density function. After defining the notion of dominant probability and probabilistic skyline, we put forward a novel algorithm to deal with continuous probabilistic skyline query on road network. Firstly, we compute the dominant probability and skyline probability to get initial permanent p -skyline set. Then we define events to predict the time when dominant relationship between moving objects may change. Furthermore, we track and calculate events to update the probabilistic skyline in an incremental way. Two pruning strategies are proposed to cancel invalid events and objects in a bid to diminish search space. Finally, an extensive experimental evaluation on real datasets shows that probabilistic skyline sets in road network can be updated by the proposed algorithm. It demonstrates both efficiency and effectiveness.https://doi.org/10.1155/2014/365064 |
| spellingShingle | Shanliang Pan Yihong Dong Jinfeng Cao Ken Chen Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network International Journal of Distributed Sensor Networks |
| title | Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network |
| title_full | Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network |
| title_fullStr | Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network |
| title_full_unstemmed | Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network |
| title_short | Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network |
| title_sort | continuous probabilistic skyline queries for uncertain moving objects in road network |
| url | https://doi.org/10.1155/2014/365064 |
| work_keys_str_mv | AT shanliangpan continuousprobabilisticskylinequeriesforuncertainmovingobjectsinroadnetwork AT yihongdong continuousprobabilisticskylinequeriesforuncertainmovingobjectsinroadnetwork AT jinfengcao continuousprobabilisticskylinequeriesforuncertainmovingobjectsinroadnetwork AT kenchen continuousprobabilisticskylinequeriesforuncertainmovingobjectsinroadnetwork |