Local Similarity Search on Geolocated Time Series Using Hybrid Indexing

TL;DR

This paper introduces a hybrid indexing approach for efficient local similarity search on geolocated time series, enabling fine-grained pattern discovery by combining spatial and local temporal similarity measures.

Contribution

It proposes the SBTSR-tree index, an extension of BTSR-tree, that segments time series temporally to improve pruning and search efficiency for local similarity queries.

Findings

SBTSR-tree significantly outperforms BTSR-tree in query speed.

Local similarity filtering uncovers more detailed temporal patterns.

Experimental results on real datasets validate the effectiveness of the proposed methods.

Abstract

Geolocated time series, i.e., time series associated with certain locations, abound in many modern applications. In this paper, we consider hybrid queries for retrieving geolocated time series based on filters that combine spatial distance and time series similarity. For the latter, unlike existing work, we allow filtering based on local similarity, which is computed based on subsequences rather than the entire length of each series, thus allowing the discovery of more fine-grained trends and patterns. To efficiently support such queries, we first leverage the state-of-the-art BTSR-tree index, which utilizes bounds over both the locations and the shapes of time series to prune the search space. Moreover, we propose optimizations that check at specific timestamps to identify candidate time series that may exceed the required local similarity threshold. To further increase pruning power, we introduce the SBTSR-tree index, an extension to BTSR-tree, which additionally segments the time series temporally, allowing the construction of tighter bounds. Our experimental results on several real-world datasets demonstrate that SBTSR-tree can provide answers much faster for all examined query types. This paper has been published in the 27th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019).