An Adaptive Parallel Method for Indexing Transportation Moving Objects
Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. T...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6645778 |
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| _version_ | 1832547724519538688 |
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| author | Kun-lun Chen Chuan-wen Li Guang Lu Jia-quan Li Tong Zhang |
| author_facet | Kun-lun Chen Chuan-wen Li Guang Lu Jia-quan Li Tong Zhang |
| author_sort | Kun-lun Chen |
| collection | DOAJ |
| description | Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects. |
| format | Article |
| id | doaj-art-a499089a990642f5a083c132704eba97 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a499089a990642f5a083c132704eba972025-02-03T06:43:46ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66457786645778An Adaptive Parallel Method for Indexing Transportation Moving ObjectsKun-lun Chen0Chuan-wen Li1Guang Lu2Jia-quan Li3Tong Zhang4School of Computer Science & Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Computer Science & Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Computer Science & Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Computer Science & Engineering, Northeastern University, Shenyang 110819, ChinaState Grid Dalian Electric Power Supply Company, Dalian, ChinaTransportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects.http://dx.doi.org/10.1155/2021/6645778 |
| spellingShingle | Kun-lun Chen Chuan-wen Li Guang Lu Jia-quan Li Tong Zhang An Adaptive Parallel Method for Indexing Transportation Moving Objects Complexity |
| title | An Adaptive Parallel Method for Indexing Transportation Moving Objects |
| title_full | An Adaptive Parallel Method for Indexing Transportation Moving Objects |
| title_fullStr | An Adaptive Parallel Method for Indexing Transportation Moving Objects |
| title_full_unstemmed | An Adaptive Parallel Method for Indexing Transportation Moving Objects |
| title_short | An Adaptive Parallel Method for Indexing Transportation Moving Objects |
| title_sort | adaptive parallel method for indexing transportation moving objects |
| url | http://dx.doi.org/10.1155/2021/6645778 |
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