  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).
