{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:10:25Z","timestamp":1783930225026,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234318","type":"print"},{"value":"9789819234325","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3432-5_33","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T07:40:59Z","timestamp":1783928459000},"page":"409-420","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TADA: An Efficient Hybrid-Storage Query-Adaptive Framework for Approximate Nearest Neighbor Search"],"prefix":"10.1007","author":[{"given":"Ran","family":"Bao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: Netvlad: Cnn architecture for weakly supervised place recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5297\u20135307 (2016)","DOI":"10.1109\/CVPR.2016.572"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Aum\u00fcller, M., Bernhardsson, E., Faithfull, A.: Ann-benchmarks: a benchmarking tool for approximate nearest neighbor algorithms. In: International conference on similarity search and applications, pp. 34\u201349. Springer (2017)","DOI":"10.1007\/978-3-319-68474-1_3"},{"issue":"4","key":"33_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3749160","volume":"3","author":"M Chatzakis","year":"2025","unstructured":"Chatzakis, M., Papakonstantinou, Y., Palpanas, T.: Darth: Declarative recall through early termination for approximate nearest neighbor search. Proceedings of the ACM on Management of Data 3(4), 1\u201326 (2025)","journal-title":"Proceedings of the ACM on Management of Data"},{"key":"33_CR4","first-page":"5199","volume":"34","author":"Q Chen","year":"2021","unstructured":"Chen, Q., et al.: Spann: highly-efficient billion-scale approximate nearest neighborhood search. Adv. Neural. Inf. Process. Syst. 34, 5199\u20135212 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp. 191\u2013198 (2016)","DOI":"10.1145\/2959100.2959190"},{"issue":"8","key":"33_CR6","first-page":"4139","volume":"44","author":"C Fu","year":"2021","unstructured":"Fu, C., Wang, C., Cai, D.: High dimensional similarity search with satellite system graph: efficiency, scalability, and unindexed query compatibility. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4139\u20134150 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"33_CR7","unstructured":"Fu, C., Xiang, C., Wang, C., Cai, D.: Fast Approximate Nearest Neighbor Search with the Navigating Spreading-Out Graph. arXiv preprint arXiv:1707.00143 (2017)"},{"key":"33_CR8","unstructured":"Gao, Y., et al.: Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv preprint arXiv:2312.10997 2(1), 32 (2023)"},{"issue":"2","key":"33_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3793926","volume":"22","author":"H Guo","year":"2026","unstructured":"Guo, H., Lu, Y.: Achieving low-latency graph-based vector search via aligning best-first search algorithm with ssd. ACM Transactions on Storage 22(2), 1\u201330 (2026)","journal-title":"ACM Transactions on Storage"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 604\u2013613 (1998)","DOI":"10.1145\/276698.276876"},{"key":"33_CR11","unstructured":"Jayaram Subramanya, S., Devvrit, F., Simhadri, H.V., Krishnawamy, R., Kadekodi, R.: Diskann: fast accurate billion-point nearest neighbor search on a single node. Advances in Neural Information Processing Systems 32 (2019)"},{"issue":"1","key":"33_CR12","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TPAMI.2010.57","volume":"33","author":"H Jegou","year":"2010","unstructured":"Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117\u2013128 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, M., Andersen, D.G., He, Y.: Improving approximate nearest neighbor search through learned adaptive early termination. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2539\u20132554 (2020)","DOI":"10.1145\/3318464.3380600"},{"issue":"4","key":"33_CR14","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1109\/TPAMI.2018.2889473","volume":"42","author":"YA Malkov","year":"2018","unstructured":"Malkov, Y.A., Yashunin, D.A.: Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Trans. Pattern Anal. Mach. Intell. 42(4), 824\u2013836 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"33_CR15","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.1109\/TPAMI.2014.2321376","volume":"36","author":"M Muja","year":"2014","unstructured":"Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227\u20132240 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert-neworks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982\u20133992 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Teofili, T., Lin, J.: Patience in proximity: a simple early termination strategy for hnsw graph traversal in approximate k-nearest neighbor search. In: European Conference on Information Retrieval, pp. 401\u2013407. Springer (2025)","DOI":"10.1007\/978-3-031-88714-7_39"},{"key":"33_CR18","unstructured":"Tian, B., Liu, H., Duan, Z., Liao, X., Jin, H., Zhang, Y.: Scalable billion-point approximate nearest neighbor search using {SmartSSDs}. In: 2024 USENIX Annual Technical Conference (USENIX ATC 24), pp. 1135\u20131150 (2024)"},{"key":"33_CR19","unstructured":"Tian, B., et al.: Fusionanns: An Efficient CPU\/GPU Cooperative Processing Architecture for Billionscale Approximate Nearest Neighbor Search. arXiv preprint arXiv:2409.16576 (2024)"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Wang, M., Xu, X., Yue, Q., Wang, Y.: A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search. arXiv preprint arXiv:2101.12631 (2021)","DOI":"10.14778\/3476249.3476255"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Xu, Y., et al.: Spfresh: Incremental in-place update for billion-scale vector search. In: Proceedings of the 29th Symposium on Operating Systems Principles, pp. 545\u2013561 (2023)","DOI":"10.1145\/3600006.3613166"},{"key":"33_CR22","unstructured":"Yang, K., et al.: Tao: A Learning Framework for Adaptive Nearest Neighbor Search Using Static Features Only. arXiv preprint arXiv:2110.00696 (2021)"},{"key":"33_CR23","unstructured":"Zhang, C., Miller, R.J.: Distribution-Aware Exploration For Adaptive Hnsw Search. arXiv preprint arXiv:2512.06636 (2025)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3432-5_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T07:41:02Z","timestamp":1783928462000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3432-5_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819234318","9789819234325"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3432-5_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}