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This is particularly important in real-time applications such as dynamic monitoring and network anomaly detection, where quick query responses are essential. To address streaming subgraph matching, existing methods incorporate precomputed indices, such as tree structures. However, these approaches often fail to scale efficiently under high query arrival rates or for large graphs due to limitations in caching, query reuse, and indexing performance. In this paper, we adopt a framework that leverages a subgraph index based on graph embeddings, enabling effective caching and reuse of query results. Building on this foundation, we perform\n            <jats:italic toggle=\"yes\">k<\/jats:italic>\n            -nearest neighbor search on high-dimensional vectors by using a vector database for indexing. Inverted file and product quantization techniques within the vector database were employed to accelerate the process. Experimental evaluations on 16 diverse real-world datasets show that our approach reduces processing time by an average of 87.7% compared to the state-of-the-art method, achieves cache hit rates ranging from 70% to 90%, and demonstrates robustness and consistency across varying batch sizes and datasets.\n          <\/jats:p>","DOI":"10.34133\/icomputing.0131","type":"journal-article","created":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T15:06:11Z","timestamp":1752073571000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Accelerating Streaming Subgraph Matching via Vector Databases"],"prefix":"10.34133","volume":"4","author":[{"given":"Liuyi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, \rHunan University, Changsha, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1763-4654","authenticated-orcid":false,"given":"Yi","family":"Ding","sequence":"additional","affiliation":[{"name":"Euler AI","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xushuo","family":"Tang","sequence":"additional","affiliation":[{"name":"Euler AI","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3546-2635","authenticated-orcid":false,"given":"Fangyue","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, \rThe University of New South Wales, Sydney, Australia."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, \rHunan University, Changsha, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, \rHunan University, Changsha, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1772-6863","authenticated-orcid":true,"given":"Zhengyi","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, \rThe University of New South Wales, Sydney, Australia."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"221","published-online":{"date-parts":[[2025,8]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3604932","article-title":"Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries","volume":"56","author":"Besta M","year":"2023","unstructured":"Besta M, Gerstenberger R, Peter E, Fischer M, Podstawski M, Barthels C, Alonso G, Hoefler T. 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