The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries

By Yufei Tao, Dimitris Papadias, Jimeng Sun. Very Large Data Bases Conference, 2003

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How researchers see this paper

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SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases (2005, background): “, [25, 26, 30]) indexes moving objects given their future trajectory movements.”
Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis (2003, methodology): “TPR*-tree [39]: The TPR*-tree uses exactly the same structure and assumptions as the TPR-tree [33].”
A Hybrid Prediction Model for Moving Objects (2008, background): “Existing approaches using linear motion functions [1], [2], [3], [4], [5] may fail to predict the location at time t2 since the functions will return position F1.”
SINA: scalable incremental processing of continuous queries in spatio-temporal databases (2004, methodology): “Future queries [24], also termed as predictive queries [26, 30], are interested in predicting the locations of moving objects.”
Monitoring k-nearest neighbor queries over moving objects (2005, background): “, the TPR ∗-tree [21]) extend R∗-tree by augmenting the indexed objects and the MBRs with velocity vectors.”