# Hercules Against Data Series Similarity Search

arXiv: https://arxiv.org/abs/2212.13297 · HTML: https://mlsanity.com/paper/2212.13297/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2022.

## Built on by (most cited first, 6 of 6)

- [Raising the ClaSS of Streaming Time Series Segmentation](https://mlsanity.com/paper/2310.20431/) (2023)
- [CLaP -- State Detection from Time Series](https://mlsanity.com/paper/2504.01783/) (2025)
- [HetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks](https://mlsanity.com/paper/2502.16288/) (2025)
- [PLASMA: A Layout-Aware Benchmark Reveals Memory Layout Matters for Graph-based ANNS on GPU](https://mlsanity.com/paper/2508.15436/) (2025)
- [All-in-one Graph-based Indexing for Hybrid Search on GPUs](https://mlsanity.com/paper/2511.00855/) (2025)
- [SEAnet: A Deep Learning Architecture for Data Series Similarity Search](https://mlsanity.com/paper/2603.01448/) (2026)

## Abstract (arXiv)

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.