SharkDB: an in-memory column-oriented storage for trajectory analysis

Bolong Zheng, Haozhou Wang, Kai Zheng, Han Su, Kuien Liu, Shuo Shang
2017 World wide web (Bussum)  
The last decade has witnessed the prevalence of sensor and GPS technologies that produce a high volume of trajectory data representing the motion history of moving objects. However some characteristics of trajectories such as variable lengths and asynchronous sampling rates make it difficult to fit into traditional database systems that are disk-based and tuple-oriented. Motivated by the success of column store and recent development of inmemory databases, we try to explore the potential
more » ... nities of boosting the performance of trajectory data processing by designing a novel trajectory storage within main memory. In contrast to most existing trajectory indexing methods that keep consecutive samples of the same trajectory in the same disk page, we partition the database into frames in which the positions of all moving objects at the same time instant are stored together and aligned in main memory. We found this column-wise storage to be surprisingly well suited for in-memory Kai Zheng
doi:10.1007/s11280-017-0466-9 fatcat:jbj6u6a56vdwxkzbxsddggkx3m