{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:22:26Z","timestamp":1783437746335,"version":"3.54.6"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031398469","type":"print"},{"value":"9783031398476","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-39847-6_7","type":"book-chapter","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T17:02:46Z","timestamp":1692291766000},"page":"99-113","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DMIS: Dual Model Index Structure for\u00a0Enhanced Performance on\u00a0Complexly Distributed Datasets"],"prefix":"10.1007","author":[{"given":"Lanzhong","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xujian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"issue":"14","key":"7_CR1","doi-asserted-by":"publisher","first-page":"1714","DOI":"10.14778\/2556549.2556556","volume":"6","author":"K Alexiou","year":"2013","unstructured":"Alexiou, K., Kossmann, D., Larson, P.\u00c5.: Adaptive range filters for cold data: avoiding trips to Siberia. Proc. VLDB Endow. 6(14), 1714\u20131725 (2013)","journal-title":"Proc. VLDB Endow."},{"key":"7_CR2","unstructured":"Bennett, J.: OpenStreetMap. Packt Publishing Ltd. (2010)"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Cooper, B.F., et al.: Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 143\u2013154 (2010)","DOI":"10.1145\/1807128.1807152"},{"key":"7_CR4","doi-asserted-by":"publisher","unstructured":"Ding, J., et al.: ALEX: an updatable adaptive learned index. In: Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, Online Conference, Portland, OR, USA, 14\u201319 June 2020, pp. 969\u2013984. ACM (2020). https:\/\/doi.org\/10.1145\/3318464.3389711","DOI":"10.1145\/3318464.3389711"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Fan, B., et al.: Cuckoo filter: practically better than bloom. In: Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, pp. 75\u201388 (2014)","DOI":"10.1145\/2674005.2674994"},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Ferragina, P., Vinciguerra, G.: The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds. Proc. VLDB Endow. 13(8), 1162\u20131175 (2020). https:\/\/doi.org\/10.14778\/3389133.3389135, https:\/\/www.vldb.org\/pvldb\/vol13\/p1162-ferragina.pdf","DOI":"10.14778\/3389133.3389135"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Galakatos, A., et al.: Fiting-tree: a data-aware index structure. In: Proceedings of the 2019 International Conference on Management of Data, pp. 1189\u20131206 (2019)","DOI":"10.1145\/3299869.3319860"},{"issue":"2","key":"7_CR8","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1145\/152610.152611","volume":"25","author":"G Graefe","year":"1993","unstructured":"Graefe, G.: Query evaluation techniques for large databases. ACM Comput. Surv. 25(2), 73\u2013169 (1993). https:\/\/doi.org\/10.1145\/152610.152611. ISSN 0360-0300","journal-title":"ACM Comput. Surv."},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Graefe, G., Larson, P.-A.: B-tree indexes and CPU caches. In: Proceedings 17th International Conference on Data Engineering, pp. 349\u2013358. IEEE (2001)","DOI":"10.1109\/ICDE.2001.914847"},{"key":"7_CR10","doi-asserted-by":"publisher","unstructured":"Gray, J., et al.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. In: Proceedings of the Twelfth International Conference on Data Engineering, pp. 152\u2013159 (1996). https:\/\/doi.org\/10.1109\/ICDE.1996.492099","DOI":"10.1109\/ICDE.1996.492099"},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Hadian, A., Heinis, T.: Interpolation-friendly B-trees: bridging the gap between algorithmic and learned indexes. In: Advances in Database Technology - 22nd International Conference on Extending Database Technology, EDBT 2019, Lisbon, Portugal, 26\u201329 March 2019, pp. 710\u2013713. OpenProceedings.org (2019). https:\/\/doi.org\/10.5441\/002\/edbt.2019.93","DOI":"10.5441\/002\/edbt.2019.93"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Kraska, T., et al.: The case for learned index structures. In: Proceedings of the 2018 international conference on management of data, pp. 489\u2013504 (2018)","DOI":"10.1145\/3183713.3196909"},{"key":"7_CR13","doi-asserted-by":"publisher","unstructured":"Lambert, D., Pinheiro, J.C.: Mining a stream of transactions for customer patterns. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, New York, NY, USA, pp. 305\u2013310. Association for Computing Machinery (2001). https:\/\/doi.org\/10.1145\/502512.502556. ISBN 158113391X","DOI":"10.1145\/502512.502556"},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Li, P., et al.: FINEdex: a fine-grained learned index scheme for scalable and concurrent memory systems. Proc. VLDB Endow. 15(2), 321\u2013334 (2021). https:\/\/doi.org\/10.14778\/3489496.3489512, https:\/\/www.vldb.org\/pvldb\/vol15\/p321-hua.pdf","DOI":"10.14778\/3489496.3489512"},{"key":"7_CR15","unstructured":"Llaveshi, A., et al.: Accelerating B+ tree search by using simple machine learning techniques. In: Proceedings of the 1st International Workshop on Applied AI for Database Systems and Applications (2019)"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Mao, Y., Kohler, E., Morris, R.T.: Cache craftiness for fast multicore key-value storage. In: Proceedings of the 7th ACM European Conference on Computer Systems, pp. 183\u2013196 (2012)","DOI":"10.1145\/2168836.2168855"},{"key":"7_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/978-3-030-30952-7_61","volume-title":"Web Information Systems and Applications","author":"W Qu","year":"2019","unstructured":"Qu, W., Wang, X., Li, J., Li, X.: Hybrid indexes by exploring traditional B-tree and linear regression. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) WISA 2019. LNCS, vol. 11817, pp. 601\u2013613. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30952-7_61"},{"issue":"3","key":"7_CR18","first-page":"96","volume":"9","author":"S Richter","year":"2015","unstructured":"Richter, S., Alvarez, V., Dittrich, J.: A seven-dimensional analysis of hashing methods and its implications on query processing. PVLDB 9(3), 96\u2013107 (2015)","journal-title":"PVLDB"},{"key":"7_CR19","unstructured":"Schraudolph, N.: Accelerated gradient descent by factor-centering decomposition. Technical report\/IDSIA, 98 (1998)"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Tang, C., et al.: XIndex: a scalable learned index for multicore data storage. In: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 308\u2013320 (2020)","DOI":"10.1145\/3332466.3374547"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Updatable learned index with precise positions. arXiv preprint arXiv:2104.05520 (2021)","DOI":"10.14778\/3457390.3457393"}],"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\/978-3-031-39847-6_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T08:57:13Z","timestamp":1729933033000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39847-6_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031398469","9783031398476"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39847-6_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 August 2023","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":"Penang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaysia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dexa.org\/dexa2023","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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"155","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":"49","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":"35","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":"32% - 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":"3","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":"For the workshops 7 full and 3 short papers have been accepted from 20 submissions","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)"}}]}}