Abstract
Metric space indexes are critical for efficient similarity searches across various applications. The Distal Spatial Approximation Tree (DiSAT) has demonstrated exceptional speed/memory trade-offs without requiring parameter tuning. However, since it operates solely on static databases, its application is limited in many exciting use cases.
This research has been dedicated to developing a dynamic version of DiSAT that allows for incremental construction. It is remarkable that the dynamic version is faster than its static counterpart. The outcome is a faster index with the same memory requirements as DiSAT. This development enhances the practicality of DiSAT, unlocking a wide range of proximity database applications.
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Chávez, E., Di Genaro, M.E., Reyes, N. (2023). Dynamic Distal Spatial Approximation Trees. In: Pesado, P. (eds) Computer Science – CACIC 2022. CACIC 2022. Communications in Computer and Information Science, vol 1778. Springer, Cham. https://doi.org/10.1007/978-3-031-34147-2_12
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