{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T07:48:38Z","timestamp":1778312918782,"version":"3.51.4"},"reference-count":98,"publisher":"Association for Computing Machinery (ACM)","issue":"9","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:p>Data series has been one of the significant data forms in various applications. It becomes imperative to devise a data series index that supports both approximate and exact similarity searches for large data series collections in high-dimensional metric spaces. The state-of-the-art works employ summarizations and indices to reduce the accesses to the data series. However, we discover two significant flaws that severely limit performance enhancement. Firstly, the state-of-the-art works often employ segment-based summarizations, whose lower bound distances decrease significantly when representing a data series collection, resulting in numerous invalid accesses. Secondly, the disk-based indices for the exact search mainly rely on tree-based indices, which results in low-quality approximate answers, consequently impacting the exact search.<\/jats:p>\n          <jats:p>To address these problems, we propose a novel solution, Double Indices and Double Summarizations (DIDS). Besides segment-based summarizations, DIDS introduces reference-point-based summarizations to improve the pruning rate by the sorted-based representation strategy. Moreover, DIDS employs reference points and a cost model to cluster similar data series, and uses a graph-based approach to interconnect various regions, enhancing approximate search capabilities. We conduct experiments on extensive datasets, validating the superior search performance of DIDS.<\/jats:p>","DOI":"10.14778\/3665844.3665851","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T22:19:07Z","timestamp":1722982747000},"page":"2198-2211","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DIDS: Double Indices and Double Summarizations for Fast Similarity Search"],"prefix":"10.14778","volume":"17","author":[{"given":"Han","family":"Hu","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiye","family":"Qiu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Liang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songling","family":"Zou","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"E. 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