VID Join： Mapping Trajectories to Points of Interest to Support Location-Based Services
Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID joi...
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| Published in | Journal of computer science and technology Vol. 30; no. 4; pp. 725 - 744 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.07.2015
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-9000 1860-4749 |
| DOI | 10.1007/s11390-015-1557-7 |
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| Abstract | Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data. |
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| AbstractList | Variable influence duration (VID) join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects (e.g., travelers), and spatial points are points of interest (POIs, e.g., restaurants). VID join returns all pairs of (τs, p) if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned (τs, p) implies that the moving object associated with τs stayed at p (e.g., having dinner at a restaurant). Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e?ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data. Variable influence duration (VID) join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects (e.g., travelers), and spatial points are points of interest (POIs, e.g., restaurants). VID join returns all pairs of ( τ s , p ) if τ s is spatially close to p for a long period of time, where τ s is a segment of trajectory τ ∈ T and p ∈ P . Each returned ( τ s , p ) implies that the moving object associated with τ s stayed at p (e.g., having dinner at a restaurant). Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p , and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join efficiently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data. Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data. Variable influence duration (VID) join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects (e.g., travelers), and spatial points are points of interest (POIs, e.g., restaurants). VID join returns all pairs of ( tau sub( )s p) if tau sub( )sis spatially close to p for a long period of time, where tau sub( )sis a segment of trajectory tau T and p P. Each returned ( tau sub( )s p) implies that the moving object associated with tau sub( )sstayed at p (e.g., having dinner at a restaurant). Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between tau and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join efficiently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data. Variable influence duration (VID) join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects (e.g., travelers), and spatial points are points of interest (POIs, e.g., restaurants). VID join returns all pairs of (τ ^sub s^, p) if τ ^sub s^ is spatially close to p for a long period of time, where τ ^sub s^ is a segment of trajectory τ T and p P. Each returned (τ ^sub s^, p) implies that the moving object associated with τ ^sub s^ stayed at p (e.g., having dinner at a restaurant). Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join efficiently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data. |
| Author | 商烁 谢珂心 郑凯 刘家俊 文继荣 |
| AuthorAffiliation | Department of Computer Science, China University of Petroleum, Beijing 102249, China School of Information Technology and Electrical Engineering, The University of Queensland Brisbane, QLD 4072, Australia School of Computer Science and Technology, Soochow University, Suzhou 215006, China Commonwealth Scientific and Industrial Research Organisation, Kenmore, QLD 4069, Australia Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing 100080, China |
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| CitedBy_id | crossref_primary_10_1016_j_neucom_2016_02_085 crossref_primary_10_1016_j_future_2022_07_003 crossref_primary_10_1016_j_compenvurbsys_2018_03_007 crossref_primary_10_1016_j_neucom_2020_03_121 crossref_primary_10_52547_jgit_9_2_105 crossref_primary_10_1007_s11280_018_0563_4 crossref_primary_10_1007_s11390_016_1653_3 crossref_primary_10_1007_s10619_020_07290_2 crossref_primary_10_1007_s11280_022_01092_5 crossref_primary_10_1109_TKDE_2018_2854705 crossref_primary_10_1016_j_bdr_2021_100221 crossref_primary_10_1080_13658816_2020_1778003 |
| Cites_doi | 10.1109/ICDE.2008.4497588 10.14778/1453856.1453971 10.1109/ICDE.2008.4497415 10.1145/1247480.1247546 10.1145/1629890.1629898 10.1145/1463434.1463477 10.1145/2370216.2370273 10.1016/B978-155860869-6/50033-0 10.1145/1206049.1206056 10.1145/2247596.2247616 10.1109/ICDE.2008.4497418 10.1145/2020408.2020462 10.1109/ICDE.2002.994759 10.1145/1341012.1341041 10.1145/335191.335427 10.1109/ICDE.2013.6544830 10.1007/s00778-014-0369-7 10.1109/ICDE.2000.839452 10.1007/11535331_21 10.1145/1629890.1629897 10.1109/ICDE.2006.36 10.1007/11535331_16 10.1145/253260.253340 10.1145/235968.233338 10.1007/3-540-63238-7_30 10.1145/1281192.1281230 10.1007/s00778-013-0331-0 10.1145/170035.170075 10.1109/ICDE.2013.6544829 10.1109/ICDE.1993.344078 10.1007/BF01840357 10.3138/FM57-6770-U75U-7727 |
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| Copyright | Springer Science+Business Media New York 2015 Springer Science+Business Media New York 2015. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Notes | Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data. trajectory, spatial database, spatial join, spatio-temporal join 11-2296/TP Shuo Shang, Kexin Xie, Kai Zheng,Jiajun Liu,Ji-Rong Wen（ 1Department of Computer Science, China University of Petroleum, Beijing 102249, China ;2School of Information Technology and Electrical Engineering, The University of Queensland Brisbane, QLD 4072, Australia ;3School of Computer Science and Technology, Soochow University, Suzhou 215006, China; 4 Commonwealth Scientific and Industrial Research Organisation, Kenmore, QLD 4069, Australia; 5Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing 100080, China） ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| References | Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma W Y. Mining user similarity based on location history. In Proc. the 16th ACM SIGSPATIAL GIS, November 2008, pp. 34:1–34:10. Günther O. Efficient computation of spatial joins. In Proc. the 9th ICDE, April 1993, pp. 50–59. Yiu M L, Mamoulis N, Karras P. Common influence join: A natural join operation for spatial pointsets. In Proc. the 24th ICDE, April 2008, pp. 100–109. Jeung H, Liu Q, Shen H T, Zhou X. A hybrid prediction model for moving objects. In Proc. the 24th ICDE, April 2008, pp. 70–79. Dittrich J P, Seeger B. Data redundancy and duplicate detection in spatial join processing. In Proc. the 16th ICDE, February 28–March 3, 2000, pp. 535–546. Xue A Y, Qi J, Xie X, Zhang R, Huang J, Li Y. Solving the data sparsity problem in destination prediction. VLDB J., 2015, 24(2): 219–243. Xue A Y, Zhang R, Zheng Y, Xie X, Huang J, Xu Z. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In Proc. the 29th ICDE, April 2013, pp. 254–265. Zheng Y, Xie X, Ma W Y. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin, 2010, 33(2): 32–39. Zhou X, Abel D J, Truffet D. Data partitioning for parallel spatial join processing. In Proc. the 5th SSTD, July 1997, pp. 178–196. Kim S W, Cho W S, Lee M J, Whang K Y. A new algorithm for processing joins using the multilevel grid file. In Proc. the 4th DASFAA, April 1995, pp. 115–123. Alvares L O, Bogorny V, Kuijpers B, de Macêdo J A F, Moelans B, Vaisman A A. A model for enriching trajectories with semantic geographical information. In Proc. the 15th ACM GIS, Nov. 2007, pp. 22:1–22:8. Šaltenis S, Jensen C S, Leutenegger S T, Lopez M A. Indexing the positions of continuously moving objects. ACM SIGMOD Record, 2000, 29(2): 331–342. Jeung H, Yiu M L, Zhou X, Jensen C S, Shen H T. Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment, 2008, 1(1): 1068–1080. Pfoser D, Jensen C S, Theodoridis Y. Novel approaches in query processing for moving object trajectories. In Proc. the 26th VLDB, September 2000, pp. 395–406. Cao H, Mamoulis N, Cheung D W. Mining frequent spatio-temporal sequential patterns. In Proc. the 5th ICDM, November 2005, pp. 82–89. Chakka V P, Everspaugh A, Patel J M. Indexing large trajectory data sets with SETI. In Proc. the 1st CIDR, January 2003. Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P. User oriented trajectory search for trip recommendation. In Proc. the 15th EDBT, March 2012, pp. 156–167. Douglas D H, Peucker T K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 1973, 10(2): 112–122. Giannotti F, Nanni M, Pinelli F, Pedreschi D. Trajectory pattern mining. In Proc. the 13th KDD, August 2007, pp. 330–339. Kalnis P, Mamoulis N, Bakiras S. On discovering moving clusters in spatio-temporal data. In Proc. the 9th SSTD, August 2005, pp. 364–381. Lee J G, Han J, Whang K Y. Trajectory clustering: A partition-and-group framework. In Proc. ACM SIGMOD, June 2007, pp. 593–604. Arumugam S, Jermaine C. Closest-point-of-approach join for moving object histories. In Proc. the 22nd ICDE, April 2006, Article No. 86. Patel J M, DeWitt D J. Partition based spatial-merge join. ACM SIGMOD Record, 1996, 25(2): 259–270. Tao Y, Papadias D, Shen Q. Continuous nearest neighbor search. In Proc. the 28th VLDB, August 2002, pp. 287–298. Brinkhoff T, Kriegel H P, Seeger B. Efficient processing of spatial joins using R-trees. In Proc. ACM SIGMOD, May 1993, pp. 237–246. Horvitz E, Krumm J. Some help on the way: Opportunistic routing under uncertainty. In Proc. the 14th ACM Ubicomp, September 2012, pp. 371–380. Shang S, Ding R, Zheng K, Jensen C S, Kalnis P, Zhou X. Personalized trajectory matching in spatial networks. VLDB J., 2014, 23(3): 449–468. Yuan J, Zheng Y, Xie X, Sun G. Driving with knowledge from the physical world. In Proc. the 17th SIGKDD, August 2011, pp. 316–324. Arge L, Procopiuc O, Ramaswamy S, Suel T, Vitter J S. Scalable sweeping-based spatial join. In Proc. the 24th VLDB, Aug. 1998, pp. 570–581. Šaltenis S, Jensen C S. Indexing of moving objects for location-based services. In Proc. the 18th ICDE, February 26-March 1, 2002, pp. 463–472. Jeung H, Shen H T, Zhou X. Convoy queries in spatio-temporal databases. In Proc. the 24th ICDE, April 2008, pp. 1457–1459. Huang Y W, Jing N, Rundensteiner E A. Spatial joins using R-trees: Breadth-first traversal with global optimizations. In Proc. the 23rd VLDB, August 1997, pp. 396–405. Koudas N, Sevcik K C. Size separation spatial join. In Proc. ACM SIGMOD, May 1997, pp. 324–335. Chen Y, Jiang K, Zheng Y, Li C, Yu N. Trajectory simplification method for location-based social networking services. In Proc. LBSN, November 2009, pp. 33–40. Zheng K, Zheng Y, Yuan N J, Shang S. On discovery of gathering patterns from trajectories. In Proc. the 29th ICDE, April 2013, pp. 242–253. Li F, Cheng D, Hadjieleftheriou M, Kollios G, Teng S H. On trip planning queries in spatial databases. In Proc. the 9th SSTD, August 2005, pp. 273–290. Jacox E H, Samet H. Spatial join techniques. ACM Trans. Database Syst., 2007, 32(1): Article No. 7. Xie K, Deng K, Zhou X. From trajectories to activities: A spatio-temporal join approach. In Proc. LBSN, November 2009, pp. 25–32. Fortune S. A sweepline algorithm for Voronoi diagrams. Algorithmica, 1987, 2: 153–174. 1557_CR7 1557_CR6 1557_CR5 1557_CR4 1557_CR3 1557_CR2 1557_CR1 1557_CR20 1557_CR21 1557_CR9 1557_CR8 1557_CR28 1557_CR29 1557_CR26 1557_CR27 1557_CR24 1557_CR25 1557_CR22 1557_CR23 1557_CR19 1557_CR31 1557_CR10 1557_CR32 1557_CR30 1557_CR17 1557_CR39 1557_CR18 1557_CR15 1557_CR37 1557_CR16 1557_CR38 1557_CR13 1557_CR35 1557_CR14 1557_CR36 1557_CR11 1557_CR33 1557_CR12 1557_CR34 |
| References_xml | – reference: Šaltenis S, Jensen C S. Indexing of moving objects for location-based services. In Proc. the 18th ICDE, February 26-March 1, 2002, pp. 463–472. – reference: Chen Y, Jiang K, Zheng Y, Li C, Yu N. Trajectory simplification method for location-based social networking services. In Proc. LBSN, November 2009, pp. 33–40. – reference: Zheng Y, Xie X, Ma W Y. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin, 2010, 33(2): 32–39. – reference: Brinkhoff T, Kriegel H P, Seeger B. Efficient processing of spatial joins using R-trees. In Proc. ACM SIGMOD, May 1993, pp. 237–246. – reference: Li F, Cheng D, Hadjieleftheriou M, Kollios G, Teng S H. On trip planning queries in spatial databases. In Proc. the 9th SSTD, August 2005, pp. 273–290. – reference: Yiu M L, Mamoulis N, Karras P. Common influence join: A natural join operation for spatial pointsets. In Proc. the 24th ICDE, April 2008, pp. 100–109. – reference: Giannotti F, Nanni M, Pinelli F, Pedreschi D. Trajectory pattern mining. In Proc. the 13th KDD, August 2007, pp. 330–339. – reference: Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma W Y. Mining user similarity based on location history. In Proc. the 16th ACM SIGSPATIAL GIS, November 2008, pp. 34:1–34:10. – reference: Arumugam S, Jermaine C. Closest-point-of-approach join for moving object histories. In Proc. the 22nd ICDE, April 2006, Article No. 86. – reference: Lee J G, Han J, Whang K Y. Trajectory clustering: A partition-and-group framework. In Proc. ACM SIGMOD, June 2007, pp. 593–604. – reference: Jeung H, Liu Q, Shen H T, Zhou X. A hybrid prediction model for moving objects. In Proc. the 24th ICDE, April 2008, pp. 70–79. – reference: Xue A Y, Qi J, Xie X, Zhang R, Huang J, Li Y. Solving the data sparsity problem in destination prediction. VLDB J., 2015, 24(2): 219–243. – reference: Cao H, Mamoulis N, Cheung D W. Mining frequent spatio-temporal sequential patterns. In Proc. the 5th ICDM, November 2005, pp. 82–89. – reference: Kalnis P, Mamoulis N, Bakiras S. On discovering moving clusters in spatio-temporal data. In Proc. the 9th SSTD, August 2005, pp. 364–381. – reference: Yuan J, Zheng Y, Xie X, Sun G. Driving with knowledge from the physical world. In Proc. the 17th SIGKDD, August 2011, pp. 316–324. – reference: Jeung H, Shen H T, Zhou X. Convoy queries in spatio-temporal databases. In Proc. the 24th ICDE, April 2008, pp. 1457–1459. – reference: Chakka V P, Everspaugh A, Patel J M. Indexing large trajectory data sets with SETI. In Proc. the 1st CIDR, January 2003. – reference: Zhou X, Abel D J, Truffet D. Data partitioning for parallel spatial join processing. In Proc. the 5th SSTD, July 1997, pp. 178–196. – reference: Jeung H, Yiu M L, Zhou X, Jensen C S, Shen H T. Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment, 2008, 1(1): 1068–1080. – reference: Jacox E H, Samet H. Spatial join techniques. ACM Trans. Database Syst., 2007, 32(1): Article No. 7. – reference: Arge L, Procopiuc O, Ramaswamy S, Suel T, Vitter J S. Scalable sweeping-based spatial join. In Proc. the 24th VLDB, Aug. 1998, pp. 570–581. – reference: Xie K, Deng K, Zhou X. From trajectories to activities: A spatio-temporal join approach. In Proc. LBSN, November 2009, pp. 25–32. – reference: Alvares L O, Bogorny V, Kuijpers B, de Macêdo J A F, Moelans B, Vaisman A A. A model for enriching trajectories with semantic geographical information. In Proc. the 15th ACM GIS, Nov. 2007, pp. 22:1–22:8. – reference: Šaltenis S, Jensen C S, Leutenegger S T, Lopez M A. Indexing the positions of continuously moving objects. ACM SIGMOD Record, 2000, 29(2): 331–342. – reference: Pfoser D, Jensen C S, Theodoridis Y. Novel approaches in query processing for moving object trajectories. In Proc. the 26th VLDB, September 2000, pp. 395–406. – reference: Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P. User oriented trajectory search for trip recommendation. In Proc. the 15th EDBT, March 2012, pp. 156–167. – reference: Xue A Y, Zhang R, Zheng Y, Xie X, Huang J, Xu Z. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In Proc. the 29th ICDE, April 2013, pp. 254–265. – reference: Dittrich J P, Seeger B. Data redundancy and duplicate detection in spatial join processing. In Proc. the 16th ICDE, February 28–March 3, 2000, pp. 535–546. – reference: Günther O. Efficient computation of spatial joins. In Proc. the 9th ICDE, April 1993, pp. 50–59. – reference: Shang S, Ding R, Zheng K, Jensen C S, Kalnis P, Zhou X. Personalized trajectory matching in spatial networks. VLDB J., 2014, 23(3): 449–468. – reference: Kim S W, Cho W S, Lee M J, Whang K Y. A new algorithm for processing joins using the multilevel grid file. In Proc. the 4th DASFAA, April 1995, pp. 115–123. – reference: Huang Y W, Jing N, Rundensteiner E A. Spatial joins using R-trees: Breadth-first traversal with global optimizations. In Proc. the 23rd VLDB, August 1997, pp. 396–405. – reference: Patel J M, DeWitt D J. Partition based spatial-merge join. ACM SIGMOD Record, 1996, 25(2): 259–270. – reference: Zheng K, Zheng Y, Yuan N J, Shang S. On discovery of gathering patterns from trajectories. In Proc. the 29th ICDE, April 2013, pp. 242–253. – reference: Douglas D H, Peucker T K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 1973, 10(2): 112–122. – reference: Tao Y, Papadias D, Shen Q. Continuous nearest neighbor search. In Proc. the 28th VLDB, August 2002, pp. 287–298. – reference: Fortune S. A sweepline algorithm for Voronoi diagrams. Algorithmica, 1987, 2: 153–174. – reference: Horvitz E, Krumm J. Some help on the way: Opportunistic routing under uncertainty. In Proc. the 14th ACM Ubicomp, September 2012, pp. 371–380. – reference: Koudas N, Sevcik K C. Size separation spatial join. In Proc. ACM SIGMOD, May 1997, pp. 324–335. – ident: 1557_CR5 doi: 10.1109/ICDE.2008.4497588 – ident: 1557_CR7 doi: 10.14778/1453856.1453971 – ident: 1557_CR28 – ident: 1557_CR6 doi: 10.1109/ICDE.2008.4497415 – ident: 1557_CR8 doi: 10.1145/1247480.1247546 – ident: 1557_CR1 – ident: 1557_CR39 doi: 10.1145/1629890.1629898 – ident: 1557_CR3 doi: 10.1145/1463434.1463477 – ident: 1557_CR36 doi: 10.1145/2370216.2370273 – ident: 1557_CR17 doi: 10.1016/B978-155860869-6/50033-0 – ident: 1557_CR20 doi: 10.1145/1206049.1206056 – ident: 1557_CR23 – ident: 1557_CR10 doi: 10.1145/2247596.2247616 – ident: 1557_CR2 doi: 10.1109/ICDE.2008.4497418 – ident: 1557_CR15 – ident: 1557_CR30 – ident: 1557_CR35 doi: 10.1145/2020408.2020462 – ident: 1557_CR13 doi: 10.1109/ICDE.2002.994759 – ident: 1557_CR32 doi: 10.1145/1341012.1341041 – ident: 1557_CR12 doi: 10.1145/335191.335427 – ident: 1557_CR34 doi: 10.1109/ICDE.2013.6544830 – ident: 1557_CR33 doi: 10.1007/s00778-014-0369-7 – ident: 1557_CR27 doi: 10.1109/ICDE.2000.839452 – ident: 1557_CR31 doi: 10.1007/11535331_21 – ident: 1557_CR19 doi: 10.1145/1629890.1629897 – ident: 1557_CR16 doi: 10.1109/ICDE.2006.36 – ident: 1557_CR38 doi: 10.1007/11535331_16 – ident: 1557_CR26 doi: 10.1145/253260.253340 – ident: 1557_CR18 doi: 10.1145/235968.233338 – ident: 1557_CR25 doi: 10.1007/3-540-63238-7_30 – ident: 1557_CR4 doi: 10.1145/1281192.1281230 – ident: 1557_CR24 – ident: 1557_CR9 doi: 10.1007/s00778-013-0331-0 – ident: 1557_CR22 doi: 10.1145/170035.170075 – ident: 1557_CR11 doi: 10.1109/ICDE.2013.6544829 – ident: 1557_CR14 – ident: 1557_CR21 doi: 10.1109/ICDE.1993.344078 – ident: 1557_CR37 doi: 10.1007/BF01840357 – ident: 1557_CR29 doi: 10.3138/FM57-6770-U75U-7727 |
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| SubjectTerms | Advertising Algorithms Artificial Intelligence Computer engineering Computer Science Data Structures and Information Theory Fast food Information Systems Applications (incl.Internet) Location based services Mapping Object motion Optimization Optimization techniques Regular Paper Restaurants Segments Social security Software Engineering Spatial data Studies Supermarkets Theory of Computation Trajectories 兴趣点 基于位置的服务 持续时间 映射 移动对象 空连接 空间点 视频 |
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| Title | VID Join： Mapping Trajectories to Points of Interest to Support Location-Based Services |
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