A Framework for Generating Network-Based Moving Objects
Geoinformatica
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Path prediction and predictive range querying in road network databases
The VLDB Journal — The International Journal on Very Large Data Bases
Panda: a predictive spatio-temporal query processor
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Predictive spatio-temporal queries: a comprehensive survey and future directions
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
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This demo presents the iRoad framework for evaluating predictive queries on moving objects for road networks. The main promise of the iRoad system is to support a variety of common predictive queries including predictive point query, predictive range query, predictive KNN query, and predictive aggregate query. The iRoad framework is equipped with a novel data structure, named reachability tree, employed to determine the reachable nodes for a moving object within a specified future time Τ. In fact, the reachability tree prunes the space around each object in order to significantly reduce the computation time. So, iRoad is able to scale up to handle real road networks with millions of nodes, and it can process heavy workloads on large numbers of moving objects. During the demo, audience will be able to interact with iRoad through a well designed Graphical User Interface to issue different types of predictive queries on a real road network, to obtain the predictive heatmap of the area of interest, to follow the creation and the dynamic update of the reachability tree around a specific moving object, and finally to examine the system efficiency and scalability.