Path–Based Continuous Spatial Keyword Queries
In this paper, we study the path based continuous spatial keyword queries, which find the answer set continuously when the query point moves on a given path. Under this setting, we explore two primitive spatial keyword queries, namely k nearest neighbor query and range query. The technical challenge...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/4091245 |
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| _version_ | 1832563498159177728 |
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| author | Fangshu Chen Pengfei Zhang Chengcheng Yu Huaizhong Lin Shan Tang Xiaoming Hu |
| author_facet | Fangshu Chen Pengfei Zhang Chengcheng Yu Huaizhong Lin Shan Tang Xiaoming Hu |
| author_sort | Fangshu Chen |
| collection | DOAJ |
| description | In this paper, we study the path based continuous spatial keyword queries, which find the answer set continuously when the query point moves on a given path. Under this setting, we explore two primitive spatial keyword queries, namely k nearest neighbor query and range query. The technical challenges lie in that: (1) retrieving qualified vertices in large road networks efficiently, and (2) issuing the query continuously for points on the path, which turns out to be inapplicable. To overcome the above challenges, we first propose a backbone road network index structure (BNI), which supports the distance computation efficiently and offers a global insights of the whole road network. Motivated by the safe zone technique, we then transform our queries to the issue of finding event points, which capture the changes of answer set. By this transformation, our queries are to be simple and feasible. To answer the queries, we propose a Two-Phase Progressive (TPP) computing framework, which first computes the answer sets for some crucial vertices on the path, and then identifies the event points by the retrieved answer sets. Extensive experiments on both real and synthetic data sets are conducted to evaluate the performance of our proposed algorithms, and the results show that our algorithms outperform competitors by several orders of magnitude. |
| format | Article |
| id | doaj-art-71a5a08c85634f78a138eaf3089d910b |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-71a5a08c85634f78a138eaf3089d910b2025-02-03T01:19:58ZengWileyComplexity1099-05262022-01-01202210.1155/2022/4091245Path–Based Continuous Spatial Keyword QueriesFangshu Chen0Pengfei Zhang1Chengcheng Yu2Huaizhong Lin3Shan Tang4Xiaoming Hu5The College of Computer and Information EngineeringThe College of Computer Science and TechnologyThe College of Computer and Information EngineeringThe College of Computer Science and Technology1th Shanghai Zhi Pan Intelligent Technology Co.Ltd.The College of Computer and Information EngineeringIn this paper, we study the path based continuous spatial keyword queries, which find the answer set continuously when the query point moves on a given path. Under this setting, we explore two primitive spatial keyword queries, namely k nearest neighbor query and range query. The technical challenges lie in that: (1) retrieving qualified vertices in large road networks efficiently, and (2) issuing the query continuously for points on the path, which turns out to be inapplicable. To overcome the above challenges, we first propose a backbone road network index structure (BNI), which supports the distance computation efficiently and offers a global insights of the whole road network. Motivated by the safe zone technique, we then transform our queries to the issue of finding event points, which capture the changes of answer set. By this transformation, our queries are to be simple and feasible. To answer the queries, we propose a Two-Phase Progressive (TPP) computing framework, which first computes the answer sets for some crucial vertices on the path, and then identifies the event points by the retrieved answer sets. Extensive experiments on both real and synthetic data sets are conducted to evaluate the performance of our proposed algorithms, and the results show that our algorithms outperform competitors by several orders of magnitude.http://dx.doi.org/10.1155/2022/4091245 |
| spellingShingle | Fangshu Chen Pengfei Zhang Chengcheng Yu Huaizhong Lin Shan Tang Xiaoming Hu Path–Based Continuous Spatial Keyword Queries Complexity |
| title | Path–Based Continuous Spatial Keyword Queries |
| title_full | Path–Based Continuous Spatial Keyword Queries |
| title_fullStr | Path–Based Continuous Spatial Keyword Queries |
| title_full_unstemmed | Path–Based Continuous Spatial Keyword Queries |
| title_short | Path–Based Continuous Spatial Keyword Queries |
| title_sort | path based continuous spatial keyword queries |
| url | http://dx.doi.org/10.1155/2022/4091245 |
| work_keys_str_mv | AT fangshuchen pathbasedcontinuousspatialkeywordqueries AT pengfeizhang pathbasedcontinuousspatialkeywordqueries AT chengchengyu pathbasedcontinuousspatialkeywordqueries AT huaizhonglin pathbasedcontinuousspatialkeywordqueries AT shantang pathbasedcontinuousspatialkeywordqueries AT xiaominghu pathbasedcontinuousspatialkeywordqueries |