Hercules Against Data Series Similarity Search

• Main Authors: Echihabi, Karima, Fatourou, Panagiota, Zoumpatianos, Kostas, Palpanas, Themis, Benbrahim, Houda
• Format: Journal Article
• Language: English
• Published: 26-12-2022
• Subjects: Computer Science - Databases
• DOI: 10.48550/arxiv.2212.13297

Proc. VLDB Endow. 15(10): 2005-2018 (2022) We propose Hercules, a parallel tree-based technique for exact similarity
search on massive disk-based data series collections. We present novel index
construction and query answering algorithms that leverage different
summarization techniques, carefully schedule costly operations, optimize memory
and disk accesses, and exploit the multi-threading and SIMD capabilities of
modern hardware to perform CPU-intensive calculations. We demonstrate the
superiority and robustness of Hercules with an extensive experimental
evaluation against state-of-the-art techniques, using many synthetic and real
datasets, and query workloads of varying difficulty. The results show that
Hercules performs up to one order of magnitude faster than the best competitor
(which is not always the same). Moreover, Hercules is the only index that
outperforms the optimized scan on all scenarios, including the hard query
workloads on disk-based datasets. This paper was published in the Proceedings
of the VLDB Endowment, Volume 15, Number 10, June 2022.