#6317. Fast data series indexing for in-memory data

September 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Information Systems;
Hardware and Architecture;
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Abstract:
Data series similarity search is a core operation for several data series analysis applications across many different domains. However, the state-of-the-art techniques fail to deliver the time performance required for interactive exploration, or analysis of large data series collections. In this work, we propose MESSI, the first data series index designed for in-memory operation on modern hardware. Our index takes advantage of the modern hardware parallelization opportunities (i.e., SIMD instructions, multi-socket and multi-core architectures), in order to accelerate both index construction and similarity search processing times. Moreover, it benefits from a careful design in the setup and coordination of the parallel workers and data structures, so that it maximizes its performance for in-memory operations. MESSI supports similarity search using both the Euclidean and dynamic time warping (DTW) distances.
Keywords:
Data series; Indexing; Modern hardware

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