{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:47:51Z","timestamp":1742924871426,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031398469"},{"type":"electronic","value":"9783031398476"}],"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_22","type":"book-chapter","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T17:02:46Z","timestamp":1692291766000},"page":"304-309","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Optimal Tree-Based Index Placement for\u00a0Autonomous Database"],"prefix":"10.1007","author":[{"given":"Xiaoyue","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianzhe","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaopeng","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Jie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.fss.2020.04.011","volume":"413","author":"RD Mol","year":"2020","unstructured":"Mol, R.D., Barranco, C.D., Tr\u00e9, G.D.: Indexing possibilistic numerical data using interval B+-trees. Fuzzy Sets Syst. 413, 138\u2013154 (2020)","journal-title":"Fuzzy Sets Syst."},{"issue":"3","key":"22_CR2","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1109\/TPDS.2019.2942918","volume":"31","author":"W Zhang","year":"2019","unstructured":"Zhang, W., Yan, Z., Lin, Y., et al.: A high throughput B+tree for SIMD architectures. IEEE Trans. Parallel and Distrib. Syst. 31(3), 707\u2013720 (2019)","journal-title":"IEEE Trans. Parallel and Distrib. Syst."},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Ziegler, T., Vani, S.T., Binnig, C., et al.: Designing distributed tree-based index structures for fast RDMA-capable networks. In: Proceedings of the 2019 International Conference on Management of Data, pp. 741\u2013758 (2019)","DOI":"10.1145\/3299869.3300081"},{"issue":"1","key":"22_CR4","first-page":"302","volume":"8","author":"B Huang","year":"2014","unstructured":"Huang, B., Yuxing, P.: An efficient distributed B-tree index method in cloud computing. Open Cybern. Syst. J. 8(1), 302\u2013308 (2014)","journal-title":"Open Cybern. Syst. J."},{"issue":"1","key":"22_CR5","doi-asserted-by":"publisher","first-page":"598","DOI":"10.14778\/1453856.1453922","volume":"1","author":"MK Aguilera","year":"2008","unstructured":"Aguilera, M.K., Golab, W., Shah, M.A.: A practical scalable distributed B-tree. Proc. VLDB Endowment 1(1), 598\u2013609 (2008)","journal-title":"Proc. VLDB Endowment"},{"issue":"9","key":"22_CR6","doi-asserted-by":"publisher","first-page":"884","DOI":"10.14778\/2311906.2311915","volume":"5","author":"B Sowell","year":"2012","unstructured":"Sowell, B., Golab, W., Shah, M.A.: Minuet: a scalable distributed multiversion B-tree. Proc. VLDB Endowment. 5(9), 884\u2013895 (2012)","journal-title":"Proc. VLDB Endowment."},{"key":"22_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-642-40148-0_12","volume-title":"Networked Systems","author":"GV Bochmann","year":"2013","unstructured":"Bochmann, G.V., Asaduzzaman, S.: Distributed B-tree with weak consistency. In: Gramoli, V., Guerraoui, R. (eds.) NETYS 2013. LNCS, vol. 7853, pp. 159\u2013174. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40148-0_12"},{"issue":"1\u20132","key":"22_CR8","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.14778\/1920841.1920991","volume":"3","author":"S Wu","year":"2010","unstructured":"Wu, S., et al.: Efficient B-tree based indexing for cloud data processing. Proc. VLDB Endowment 3(1\u20132), 1207\u20131218 (2010)","journal-title":"Proc. VLDB Endowment"},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s10586-013-0246-y","volume":"17","author":"W Zhou","year":"2014","unstructured":"Zhou, W., et al.: SNB-index: a SkipNet and B plus tree based auxiliary Cloud index. Cluster Comput. 17, 453\u2013462 (2014)","journal-title":"Cluster Comput."},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.future.2017.03.028","volume":"73","author":"H Singh","year":"2017","unstructured":"Singh, H., Bawa, S., et al.: A MapReduce-based scalable discovery and indexing of structured big data. Future Gener. Comput. Syst. 73, 32\u201343 (2017)","journal-title":"Future Gener. Comput. Syst."},{"key":"22_CR11","first-page":"1","volume":"54","author":"W Du","year":"2020","unstructured":"Du, W., Ding, S.: A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications. Artif. Intell. Rev. 54, 1\u201324 (2020)","journal-title":"Artif. Intell. Rev."},{"issue":"2","key":"22_CR12","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1145\/276305.276337","volume":"27","author":"S Chaudhuri","year":"1998","unstructured":"Chaudhuri, S., Narasayya, V.: AutoAdmin \u201cwhat-if\u2019\u2019 index analysis utility. ACM SIGMOD Rec. 27(2), 367\u2013378 (1998)","journal-title":"ACM SIGMOD Rec."},{"key":"22_CR13","doi-asserted-by":"publisher","first-page":"115978","DOI":"10.1016\/j.eswa.2021.115978","volume":"187","author":"J Lin","year":"2022","unstructured":"Lin, J., Li, Y.Y., Song, H.B.: Semiconductor final testing scheduling using Q-learning based hyper-heuristic. Expert Syst. Appl. 187, 115978 (2022)","journal-title":"Expert Syst. Appl."},{"key":"22_CR14","doi-asserted-by":"publisher","first-page":"48742","DOI":"10.1109\/ACCESS.2021.3068407","volume":"9","author":"F Ahmed","year":"2021","unstructured":"Ahmed, F., Cho, H.S.: A time-slotted data gathering medium access control protocol using Q-learning for underwater acoustic sensor networks. IEEE Access. 9, 48742\u201348752 (2021)","journal-title":"IEEE Access."},{"key":"22_CR15","unstructured":"TPC: TPC-H benchmark. http:\/\/www.tpc.org\/tpch\/"}],"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_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:29:27Z","timestamp":1710332967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39847-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031398469","9783031398476"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39847-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}