Parallel Indexing for Past, Current and Future Locations of Moving Objects

Abstract

Facing huge amounts of location and trajectory data of moving objects, although cloud database systems based on Key-Value mechanism could perform better in scalability than traditional spatio-temporal database systems, it could not provide efficient access method to support querying the locations of moving objects in past, current and future. A parallel index method for past, current and future locations of moving objects named PIPCF is proposed. It splits the space into areas and uses Quad-Tree to manage them first, and then combines temporal property of the data to index the moving objects in each area by R-Tree. Furthermore, a hash table is used to help managing predicted trajectory unit to accelerate the index updating. The experiment shows that PIPCF could perform well in cloud computing environment and improve the querying performance of the locations of moving objects in past, current and future.