A SPATIOTEMPORAL AGGREGATION QUERY METHOD USING MULTI-THREAD PARALLEL TECHNIQUE BASED ON REGIONAL DIVISION

Existing spatiotemporal database supports spatiotemporal aggregation query over massive moving objects datasets. Due to the large amounts of data and single-thread processing method, the query speed cannot meet the application requirements. On the other hand, the query efficiency is more sensitive to spatial variation then temporal variation. In this paper, we proposed a spatiotemporal aggregation query method using multi-thread parallel technique based on regional divison and implemented it on the server. Concretely, we divided the spatiotemporal domain into several spatiotemporal cubes, computed spatiotemporal aggregation on all cubes using the technique of multi-thread parallel processing, and then integrated the query results. By testing and analyzing on the real datasets, this method has improved the query speed significantly.

[1]  Richard T. Snodgrass,et al.  Spatiotemporal aggregate computation: a survey , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[3]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[4]  Gennady Andrienko,et al.  A General Framework for Using Aggregation in Visual Exploration of Movement Data , 2010 .

[5]  Hae-Young Bae,et al.  aCN-RB-tree: Update Method for Spatio-Temporal Aggregation of Moving Object Trajectory in Ubiquitous Environment , 2009, 2009 International Conference on Computational Science and Its Applications.

[6]  Guangzhong Sun,et al.  Driving with knowledge from the physical world , 2011, KDD.

[7]  Jeffrey Considine,et al.  Spatio-temporal aggregation using sketches , 2004, Proceedings. 20th International Conference on Data Engineering.

[8]  Jimeng Sun,et al.  Querying about the past, the present, and the future in spatio-temporal databases , 2004, Proceedings. 20th International Conference on Data Engineering.

[9]  Panos Kalnis,et al.  Indexing spatio-temporal data warehouses , 2002, Proceedings 18th International Conference on Data Engineering.

[10]  Yufei Tao,et al.  Historical spatio-temporal aggregation , 2005, TOIS.

[11]  Gennady L. Andrienko,et al.  Visual analytics of movement: An overview of methods, tools and procedures , 2013, Inf. Vis..

[12]  Renzo Orsini,et al.  Approximate Aggregations in Trajectory Data Warehouses , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.