Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future ...Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.展开更多
The task of selecting the most appropriate method for indexing the data according to application requires a careful comparison study of indices of interests. In particular, we consider object movements by tracing thei...The task of selecting the most appropriate method for indexing the data according to application requires a careful comparison study of indices of interests. In particular, we consider object movements by tracing their trajectories within a predefined road network. MV3DR-tree and 3DR-tree constitute our first group indexing the objects moving in free movement scenarios. Besides, Mapping and MON-tree are the second group indexing the locations of objects moving over a network of road. Those access methods mainly organize a group of R-tree in order to index the underlying road network and the object movements. Our goal in this study is to evaluate existing proposals under fair circumstances with respect to storage consumption and spatio-temporal query execution performance. In our comparisons, we discuss the structure＇s sensibility to query＇s spatial and/or temporal extent as well as the tradeoff arising between two groups in terms of reliability and disk access performance. We believe that revealing the vulnerabilities of the selected structures, especially Mapping and MON-tree motivates us to design more robust organizations.展开更多
基金Partly supported by the National Natural Science Foundation of China （Grant No. 60573091）, the Key Project of Ministry of Education of China （Grant No. 03044）, Program for New Century Excellent Talents in University （NCET）, Program for Creative Ph.D. Thesis in University. Acknowledgments The authors would like to thank Hai-Xun Wang from IBM T. J. Watson Research, Karine Zeitouni from PRISM, Versailles Saint- Quentin University in France and Stephane Grumbach from CNRS, LIAMA China for many helpful advices.
文摘Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.
文摘The task of selecting the most appropriate method for indexing the data according to application requires a careful comparison study of indices of interests. In particular, we consider object movements by tracing their trajectories within a predefined road network. MV3DR-tree and 3DR-tree constitute our first group indexing the objects moving in free movement scenarios. Besides, Mapping and MON-tree are the second group indexing the locations of objects moving over a network of road. Those access methods mainly organize a group of R-tree in order to index the underlying road network and the object movements. Our goal in this study is to evaluate existing proposals under fair circumstances with respect to storage consumption and spatio-temporal query execution performance. In our comparisons, we discuss the structure＇s sensibility to query＇s spatial and/or temporal extent as well as the tradeoff arising between two groups in terms of reliability and disk access performance. We believe that revealing the vulnerabilities of the selected structures, especially Mapping and MON-tree motivates us to design more robust organizations.
