X-FIST: Extended flood index for efficient similarity search in massive trajectory dataset

Abstract

Similarity search tasks in big trajectory datasets often require tree-based indices to shorten the query time through early pruning of dissimilar trajectories early. However, tree-based indices have been outperformed by the learned index in skewed-distribution datasets of multidimensional point experimentally. The learned index performed faster because of its data distribution awareness and machine learning model-based prediction. Directly applying learned index to trajectories can lead to inefficient query performance due to repeating range queries according to the query trajectory length. Thus, we develop X-FIST, an extended Flood index to learn the Minimum Bounding Region of the trajectories and their sub-trajectories. In similarity search, X-FIST prunes dissimilar trajectories effectively independent to the query trajectory length. If the trajectory similarity distance function changes, X-FIST does not need to train new models of its Flood index. The experimental results on three real-world trajectory datasets demonstrate that our approach shortened query time in every distance function and produced better storage size reduction than the tree-based index and direct approach of learned index.