TY - GEN
T1 - TMN-tree
T2 - 10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable Computing and Communications, ScalCom-2010
AU - Chang, Jae Woo
AU - Song, Myoung Seon
AU - Um, Jung Ho
PY - 2010
Y1 - 2010
N2 - Because moving objects usually move on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks, like FNR-tree and MON-tree. But, because both FNR-tree and MON-tree store the moving object's segment, they can not support a spatio-temporal range query and a similar trajectory query. In this paper, we propose an efficient trajectory index structure for moving objects, named TMN-Tree (Trajectory of Moving objects on Network Tree), which can support not only a range query but also a similar trajectory query. In addition, we present query processing algorithms to support them. Main advantages of the TMN-tree are as follows; i) storing temporal data and spatial data in separate structures, ii) preserving the entire trajectories of moving objects, and iii) providing efficient trajectory-based query processing algorithms. Finally, we show that our trajectory index structure outperforms existing trajectory index structures, like FNR-Tree and MON-Tree.
AB - Because moving objects usually move on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks, like FNR-tree and MON-tree. But, because both FNR-tree and MON-tree store the moving object's segment, they can not support a spatio-temporal range query and a similar trajectory query. In this paper, we propose an efficient trajectory index structure for moving objects, named TMN-Tree (Trajectory of Moving objects on Network Tree), which can support not only a range query but also a similar trajectory query. In addition, we present query processing algorithms to support them. Main advantages of the TMN-tree are as follows; i) storing temporal data and spatial data in separate structures, ii) preserving the entire trajectories of moving objects, and iii) providing efficient trajectory-based query processing algorithms. Finally, we show that our trajectory index structure outperforms existing trajectory index structures, like FNR-Tree and MON-Tree.
KW - Index structure
KW - Spatial network database
KW - Trajectory
UR - https://www.scopus.com/pages/publications/78249232292
U2 - 10.1109/CIT.2010.289
DO - 10.1109/CIT.2010.289
M3 - Conference paper
AN - SCOPUS:78249232292
SN - 9780769541082
T3 - Proceedings - 10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, ScalCom-2010
SP - 1633
EP - 1638
BT - Proceedings - 10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, ScalCom-2010
Y2 - 29 June 2010 through 1 July 2010
ER -