{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:50:02Z","timestamp":1782003002736,"version":"3.54.5"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031087509","type":"print"},{"value":"9783031087516","type":"electronic"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08751-6_53","type":"book-chapter","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T02:03:15Z","timestamp":1655776995000},"page":"734-748","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Elastic Resource Allocation Based on\u00a0Dynamic Perception of\u00a0Operator Influence Domain in\u00a0Distributed Stream Processing"],"prefix":"10.1007","author":[{"given":"Fan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weilin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weimin","family":"Mu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyuan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,15]]},"reference":[{"key":"53_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, S., He, J., Zhou, A.C., He, B.: Briskstream: scaling data stream processing on shared-memory multicore architectures. In: Boncz, P.A., Manegold, S., Ailamaki, A., Deshpande, A., Kraska, T. (eds.) Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, Amsterdam, The Netherlands, June 30\u20135 July 2019, pp. 705\u2013722. ACM (2019)","DOI":"10.1145\/3299869.3300067"},{"key":"53_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/978-3-030-50371-0_11","volume-title":"Computational Science \u2013 ICCS 2020","author":"W Mu","year":"2020","unstructured":"Mu, W., Jin, Z., Zhu, W., Liu, F., Li, Z., Zhu, Z., Wang, W.: QEScalor: quantitative elastic scaling framework in distributed streaming processing. In: Krzhizhanovskaya, V.V., Z\u00e1vodszky, G., Lees, M.H., Dongarra, J.J., Sloot, P.M.A., Brissos, S., Teixeira, J. (eds.) ICCS 2020. LNCS, vol. 12137, pp. 147\u2013160. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50371-0_11"},{"issue":"7","key":"53_CR3","doi-asserted-by":"publisher","first-page":"724","DOI":"10.14778\/3317315.3317316","volume":"12","author":"M Borkowski","year":"2019","unstructured":"Borkowski, M., Hochreiner, C., Schulte, S.: Minimizing cost by reducing scaling operations in distributed stream processing. Proc. VLDB Endow. 12(7), 724\u2013737 (2019)","journal-title":"Proc. VLDB Endow."},{"key":"53_CR4","doi-asserted-by":"crossref","unstructured":"Hung, B.D.T., Omori, T., Ohnishi, T.: Ripple effect analysis of data flow requirements. In: van Sinderen, M., Maciaszek, L.A. (eds.) Proceedings of the 14th International Conference on Software Technologies, ICSOFT 2019, Prague, Czech Republic, 26\u201328 July, 2019, pp. 262\u2013269. SciTePress (2019)","DOI":"10.5220\/0007917902620269"},{"issue":"3","key":"53_CR5","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1109\/TPDS.2017.2762683","volume":"29","author":"F Lombardi","year":"2018","unstructured":"Lombardi, F., Aniello, L., Bonomi, S., Querzoni, L.: Elastic symbiotic scaling of operators and resources in stream processing systems. IEEE Trans. Parallel Distributed Syst. 29(3), 572\u2013585 (2018)","journal-title":"IEEE Trans. Parallel Distributed Syst."},{"issue":"7","key":"53_CR6","doi-asserted-by":"publisher","first-page":"1628","DOI":"10.1109\/TPDS.2019.2891587","volume":"30","author":"X Wei","year":"2019","unstructured":"Wei, X., Li, L., Li, X., Wang, X., Gao, S., Li, H.: Pec: proactive elastic collaborative resource scheduling in data stream processing. IEEE Trans. Parallel Distributed Syst. 30(7), 1628\u20131642 (2019)","journal-title":"IEEE Trans. Parallel Distributed Syst."},{"issue":"1","key":"53_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"53_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-030-30709-7_10","volume-title":"Network and Parallel Computing","author":"W Mu","year":"2019","unstructured":"Mu, W., Jin, Z., Wang, J., Zhu, W., Wang, W.: BGElasor: elastic-scaling framework for distributed streaming processing with deep neural network. In: Tang, X., Chen, Q., Bose, P., Zheng, W., Gaudiot, J.-L. (eds.) NPC 2019. LNCS, vol. 11783, pp. 120\u2013131. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30709-7_10"},{"key":"53_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/978-3-030-79478-1_30","volume-title":"Network and Parallel Computing","author":"F Liu","year":"2021","unstructured":"Liu, F., Jin, Z., Mu, W., Zhu, W., Zhang, Y., Wang, W.: DROAllocator: a dynamic resource-aware operator allocation\u00a0framework in distributed streaming processing. In: He, X., Shao, E., Tan, G. (eds.) NPC 2020. LNCS, vol. 12639, pp. 349\u2013360. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-79478-1_30"},{"key":"53_CR10","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/978-1-4615-5529-2_8","volume-title":"Learning to Learn","author":"S Thrun","year":"1998","unstructured":"Thrun, S.: Lifelong Learning Algorithms. In: Thrun, S., Pratt, L. (eds.) Learning to Learn, pp. 181\u2013209. Springer, Boston (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5529-2_8"},{"key":"53_CR11","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings, OpenReview.net (2017)"},{"issue":"5","key":"53_CR12","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001)","journal-title":"Ann. Stat."},{"key":"53_CR13","doi-asserted-by":"crossref","unstructured":"Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., Cottrell, G.W.: A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19\u201325 August, 2017, pp. 2627\u20132633 (2017)","DOI":"10.24963\/ijcai.2017\/366"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08751-6_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:10:31Z","timestamp":1782000631000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08751-6_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031087509","9783031087516"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08751-6_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"21 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2022","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":"iccs-computsci2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2022\/","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":"474","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":"175","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":"78","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":"37% - 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":"2.8","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":"3","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}