Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Selectivity estimation for spatio-temporal queries to moving objects
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
Geoinformatica
Modeling and Querying Moving Objects
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Spatial queries in dynamic environments
ACM Transactions on Database Systems (TODS)
A predictive location model for location-based services
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Analysis of predictive spatio-temporal queries
ACM Transactions on Database Systems (TODS)
Querying about the Past, the Present, and the Future in Spatio-Temporal Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A generic framework for monitoring continuous spatial queries over moving objects
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Proceedings of the 6th international conference on Mobile data management
Nearest and reverse nearest neighbor queries for moving objects
The VLDB Journal — The International Journal on Very Large Data Bases
Predictive Join Processing between Regions and Moving Objects
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic moving range query over RFID spatio-temporal data streams
Proceedings of the 18th ACM conference on Information and knowledge management
Effectively indexing uncertain moving objects for predictive queries
Proceedings of the VLDB Endowment
Path prediction of moving objects on road networks through analyzing past trajectories
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Optimized algorithms for predictive range and KNN queries on moving objects
Information Systems
Path prediction and predictive range querying in road network databases
The VLDB Journal — The International Journal on Very Large Data Bases
Distributed continuous range query processing on moving objects
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Predictive spatio-temporal queries: a comprehensive survey and future directions
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Opportunistic spatio-temporal event processing for mobile situation awareness
Proceedings of the 7th ACM international conference on Distributed event-based systems
iRoad: a framework for scalable predictive query processing on road networks
Proceedings of the VLDB Endowment
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This paper presents the Panda system for efficient support of a wide variety of predictive spatio-temporal queries that are widely used in several applications including traffic management, location-based advertising, and ride sharing. Unlike previous attempts in supporting predictive queries, Panda targets long-term query prediction as it relies on adapting a well-designed long-term prediction function to: (a) scale up to large number of moving objects, and (b) support large number of predictive queries. As a means of scalability, Panda smartly precomputes parts of the most frequent incoming predictive queries, which significantly reduces the query response time. Panda employs a tunable threshold that achieves a trade-off between query response time and the maintenance cost of precomptued answers. Experimental results, based on large data sets, show that Panda is scalable, efficient, and as accurate as its underlying prediction function.