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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).

Time series exploration and mining has many applications across several industrial and scientific domains. In this paper, we consider the problem of detecting locally similar pairs and groups, called bundles, over co-evolving time series. These are pairs or groups of subsequences whose values do not differ by more than {\epsilon} for at least delta consecutive timestamps, thus indicating common local patterns and trends. We first present a baseline algorithm that performs a sweep line scan across all timestamps to identify matches. Then, we propose a filter-verification technique that only examines candidate matches at judiciously chosen checkpoints across time. Specifically, we introduce two block scanning algorithms for discovering local pairs and bundles respectively, which leverage the potential of checkpoints to aggressively prune the search space. We experimentally evaluate our methods against real-world and synthetic datasets, demonstrating a speed-up in execution time by an order of magnitude over the baseline. This paper has been published in the 16th International Symposium on Spatial and Temporal Databases (SSTD19)., Comment: 16 pages, 16 figures

We address the problem of subsequence search in time series using Chebyshev distance, to which we refer as twin subsequence search. We first show how existing time series indices can be extended to perform twin subsequence search. Then, we introduce TS-Index, a novel index tailored to this problem. Our experimental evaluation compares these approaches against real time series datasets, and demonstrates that TS-Index can retrieve twin subsequences much faster under various query conditions. This paper has been published in the 24th International Conference on Extending Database Technology (EDBT 2021)., Comment: 6 pages, 8 figures