An Adaptive Large-Scale Trajectory Index for Cloud-Based Moving Object Applications.

The cutting-edge cloud-based computing platforms are a typical solution for the tremendous volumes of the moving object trajectories and the vast trajectory-driven applications. However, many challenges have been raised by the adopted distributed platforms, the nature of the trajectories, the diversity in query types, the enormous options of computing resources, etc. We propose a Dynamic Moving Object Index that is able to adapt to the changes in a dynamic environment while maximizing the benefits out of the available resources without any fine-tuning. It balances the index structure between the spatial and object localities in order to control the parallelism capacity, the communication overhead, and the computation distribution. The proposed index has innovative global and local indexes that implement several optimization approaches in order to contain the impact of balancing the locality pivot in a dynamic environment. We also conduct extensive experiments on two datasets and various queries including space-based, time-based, and object-based query types. The experiment study shows a significant performance improvement compared to existing indexing schemes.