{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T18:46:05Z","timestamp":1761417965137,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031124259"},{"type":"electronic","value":"9783031124266"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-12426-6_17","type":"book-chapter","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T16:10:03Z","timestamp":1659024603000},"page":"216-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Error-Bounded Space-Efficient Hybrid Learned Index with\u00a0High Lookup Performance"],"prefix":"10.1007","author":[{"given":"Yuquan","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xujian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiquan","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"17_CR1","unstructured":"Bingmann, T.: STX B+ Tree (2013). https:\/\/panthema.net\/2007\/stx-btree"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Ding, J., et al.: ALEX: an updatable adaptive learned index. In: SIGMOD, pp. 969\u2013984 (2020)","DOI":"10.1145\/3318464.3389711"},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.14778\/3389133.3389135","volume":"13","author":"P Ferragina","year":"2020","unstructured":"Ferragina, P., Vinciguerra, G.: The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds. Proc. VLDB Endow. 13, 1162\u20131175 (2020)","journal-title":"Proc. VLDB Endow."},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Galakatos, A., Markovitch, M., Binnig, C., Fonseca, R., Kraska, T.: Fiting-tree: a data-aware index structure. In: SIGMOD, pp. 1189\u20131206 (2019)","DOI":"10.1145\/3299869.3319860"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Kipf, A., et al.: RadixSpline: a single-pass learned index. In: aiDM@SIGMOD, pp. 1\u20135 (2020)","DOI":"10.1145\/3401071.3401659"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Kraska, T., Beutel, A., Chi, E.H., Dean, J., Polyzotis, N.: The case for learned index structures. In: SIGMOD, pp. 489\u2013504 (2018)","DOI":"10.1145\/3183713.3196909"},{"key":"17_CR7","unstructured":"Li, P., Hua, Y., Zuo, P., Jia, J.: A scalable learned index scheme in storage systems. ArXiv abs\/1905.06256 (2019)"},{"key":"17_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-030-18590-9_6","volume-title":"Database Systems for Advanced Applications","author":"X Li","year":"2019","unstructured":"Li, X., Li, J., Wang, X.: ASLM: adaptive single layer model for learned index. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11448, pp. 80\u201395. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-18590-9_6"},{"key":"17_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14778\/3421424.3421425","volume":"14","author":"R Marcus","year":"2020","unstructured":"Marcus, R., et al.: Benchmarking learned indexes. Proc. VLDB Endow. 14, 1\u201313 (2020)","journal-title":"Proc. VLDB Endow."},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Marcus, R., Zhang, E., Kraska, T.: CDFShop: exploring and optimizing learned index structures. In: SIGMOD, pp. 2789\u20132792 (2020)","DOI":"10.1145\/3318464.3384706"},{"key":"17_CR11","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s002360050048","volume":"33","author":"PE O\u2019Neil","year":"2009","unstructured":"O\u2019Neil, P.E., Cheng, E.Y.C., Gawlick, D., O\u2019Neil, E.J.: The log-structured merge-tree (LSM-tree). Acta Informatica 33, 351\u2013385 (2009). https:\/\/doi.org\/10.1007\/s002360050048","journal-title":"Acta Informatica"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Tang, C., et al.: XIndex: a scalable learned index for multicore data storage. In: PPoPP, pp. 308\u2013320 (2020)","DOI":"10.1145\/3332466.3374547"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tang, C., Wang, Z., Chen, H.: SIndex: a scalable learned index for string keys. In: APSys, pp. 17\u201324 (2020)","DOI":"10.1145\/3409963.3410496"},{"key":"17_CR14","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.14778\/3457390.3457393","volume":"14","author":"J Wu","year":"2021","unstructured":"Wu, J., Zhang, Y., Chen, S., Wang, J., Chen, Y., Xing, C.: Updatable learned index with precise positions. Proc. VLDB Endow. 14, 1276\u20131288 (2021)","journal-title":"Proc. VLDB Endow."},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s00778-014-0355-0","volume":"23","author":"Q Xie","year":"2014","unstructured":"Xie, Q., Pang, C., Zhou, X., Zhang, X., Deng, K.: Maximum error-bounded piecewise linear representation for online stream approximation. VLDB J. 23, 915\u2013937 (2014). https:\/\/doi.org\/10.1007\/s00778-014-0355-0","journal-title":"VLDB J."},{"key":"17_CR16","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s11390-021-1348-2","volume":"36","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., Jin, P., Wang, X., Lv, Y., Wan, S., Xie, X.: COLIN: a cache-conscious dynamic learned index with high read\/write performance. J. Comput. Sci. Technol. 36, 721\u2013740 (2021). https:\/\/doi.org\/10.1007\/s11390-021-1348-2","journal-title":"J. Comput. Sci. Technol."}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12426-6_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:29:14Z","timestamp":1710329354000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12426-6_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031124259","9783031124266"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12426-6_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"29 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dexa.org\/dexa2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Mixed review process- Single and double blind","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}