Efficient in-memory indexing of network-constrained trajectories

Abstract

With the decreasing cost and growing size of main memory, it is increasingly relevant to utilize main-memory indexing for efficient query processing. We propose SPNET, which we believe is the first in-memory index for network-constrained trajectory data. To exploit the main-memory setting SPNET exploits efficient shortest-path compression of trajectories to achieve a compact index structure. SPNET is capable of exploiting the parallel computing capabilities of modern machines and supports both intra- and inter-query parallelism. The former improves response time, and the latter improves throughput. By design, SPNET supports a wider range of query types than any single existing index. An experimental study in a real-world setting with 1.94 billion GPS records and nearly 4 million trajectories in a road network with 1.8 million edges indicates that SPNET typically offers performance improvements over the best existing indexes of 1.5 to 2 orders of magnitude.