{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:28:58Z","timestamp":1773329338367,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030865221","type":"print"},{"value":"9783030865238","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86523-8_10","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T06:05:16Z","timestamp":1631253916000},"page":"151-166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Very Fast Streaming Submodular Function Maximization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2780-3618","authenticated-orcid":false,"given":"Sebastian","family":"Buschj\u00e4ger","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp-Jan","family":"Honysz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukas","family":"Pfahler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1153-5986","authenticated-orcid":false,"given":"Katharina","family":"Morik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"key":"10_CR1","first-page":"1742","volume":"15","author":"A Ashkan","year":"2015","unstructured":"Ashkan, A., Kveton, B., Berkovsky, S., Wen, Z.: Optimal greedy diversity for recommendation. IJCAI. 15, 1742\u20131748 (2015)","journal-title":"IJCAI."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Badanidiyuru, A., Mirzasoleiman, B., Karbasi, A., Krause, A.: Streaming submodular maximization: massive data summarization on the fly. In: ACM SIGKDD (2014)","DOI":"10.1145\/2623330.2623637"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Brown, L.D., Cai, T.T., DasGupta, A.: Interval estimation for a binomial proportion. Statist. Sci. 16(2), 101\u2013117 (2001)","DOI":"10.1214\/ss\/1009213286"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Buchbinder, N., Feldman, M., Schwartz, R.: Online submodular maximization with preemption. ACM Trans. Algorithms 15(3), 1\u201331 (2019)","DOI":"10.1145\/3309764"},{"key":"10_CR5","unstructured":"Buschj\u00e4ger, S., Morik, K., Schmidt, M.: Summary extraction on data streams in embedded systems. In: ECML Conference Workshop IoT Large Scale Learning from Data Streams (2017)"},{"key":"10_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1007\/978-3-030-67667-4_29","volume-title":"Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track","author":"S Buschj\u00e4ger","year":"2021","unstructured":"Buschj\u00e4ger, S., Pfahler, L., Buss, J., Morik, K., Rhode, W.: On-site gamma-hadron separation with deep learning on FPGAs. In: Dong, Y., Mladeni\u0107, D., Saunders, C. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12460, pp. 478\u2013493. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67667-4_29"},{"issue":"4","key":"10_CR7","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s10618-015-0444-8","volume":"30","author":"GO Campos","year":"2016","unstructured":"Campos, G.O., et al.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Discovery 30(4), 891\u2013927 (2016). https:\/\/doi.org\/10.1007\/s10618-015-0444-8","journal-title":"Data Min. Knowl. Discovery"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Chakrabarti, A., Kale, S.: Submodular maximization meets streaming: matchings, matroids, and more. In: Integer Programming and Combinatorial Optimization, pp. 210\u2013221 (2014)","DOI":"10.1007\/978-3-319-07557-0_18"},{"key":"10_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1007\/978-3-662-47672-7_26","volume-title":"Automata, Languages, and Programming","author":"C Chekuri","year":"2015","unstructured":"Chekuri, C., Gupta, S., Quanrud, K.: Streaming algorithms for submodular function maximization. In: Halld\u00f3rsson, M.M., Iwama, K., Kobayashi, N., Speckmann, B. (eds.) ICALP 2015. LNCS, vol. 9134, pp. 318\u2013330. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-47672-7_26"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Dal Pozzolo, A., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: 2015 IEEE SSCI (2015). https:\/\/www.kaggle.com\/mlg-ulb\/creditcardfraud","DOI":"10.1109\/SSCI.2015.33"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Feige, U.: A threshold of ln n for approximating set cover. J. ACM 45(4), 634\u2013652 (1998)","DOI":"10.1145\/285055.285059"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Feldman, M., Norouzi-Fard, A., Svensson, O., Zenklusen, R.: The one-way communication complexity of submodular maximization with applications to streaming and robustness. In: 52nd Annual ACM SIGACT STOC, pp. 1363\u20131374 (2020)","DOI":"10.1145\/3357713.3384286"},{"key":"10_CR13","unstructured":"Gomes, R., Krause, A.: Budgeted nonparametric learning from data streams. In: ICML, vol. 1, p. 3 (2010)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Graf, A.B., Borer, S.: Normalization in support vector machines. In: DAGM Symposium of Pattern Recognition (2001)","DOI":"10.1007\/3-540-45404-7_37"},{"issue":"2","key":"10_CR15","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/00031305.1997.10473947","volume":"51","author":"BD Jovanovic","year":"1997","unstructured":"Jovanovic, B.D., Levy, P.S.: A look at the rule of three. Am. Statist. 51(2), 137\u2013139 (1997)","journal-title":"Am. Statist."},{"key":"10_CR16","unstructured":"Kazemi, E., Mitrovic, M., Zadimoghaddam, M., Lattanzi, S., Karbasi, A.: Submodular streaming in all its glory: tight approximation, minimum memory and low adaptive complexity. In: ICML, pp. 3311\u20133320 (2019)"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Krause, A., Golovin, D.: Submodular function maximization. Tractability 3, 71\u2013104 (2014)","DOI":"10.1017\/CBO9781139177801.004"},{"key":"10_CR18","unstructured":"Kuhnle, A.: Quick streaming algorithms for maximization of monotone submodular functions in linear time. In: International Conference on Artificial Intelligence and Statistics, pp. 1360\u20131368 (2021)"},{"key":"10_CR19","unstructured":"Kulkarni, R.: The examiner - spam clickbait catalog - 6 years of crowd sourced journalism (2017). https:\/\/www.kaggle.com\/therohk\/examine-the-examiner"},{"key":"10_CR20","unstructured":"Kulkarni, R.: A million news headlines - news headlines published over a period of 17 years (2017), https:\/\/www.kaggle.com\/therohk\/million-headlines"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422. IEEE (2008). http:\/\/odds.cs.stonybrook.edu\/forestcovercovertype-dataset\/","DOI":"10.1109\/ICDM.2008.17"},{"key":"10_CR22","unstructured":"Mirzasoleiman, B., Badanidiyuru, A., Karbasi, A.: Fast constrained submodular maximization: personalized data summarization. In: ICML, pp. 1358\u20131367 (2016)"},{"key":"10_CR23","doi-asserted-by":"publisher","unstructured":"Nemhauser, G., et al.: An analysis of approximations for maximizing submodular set functions-i. Mathematical Programming (1978). https:\/\/doi.org\/10.1007\/BF01588971","DOI":"10.1007\/BF01588971"},{"key":"10_CR24","unstructured":"Norouzi-Fard, A., Tarnawski, J., Mitrovic, S., Zandieh, A., Mousavifar, A., Svensson, O.: Beyond 1\/2-approximation for submodular maximization on massive data streams. In: ICML, pp. 3829\u20133838 (2018)"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Roady, R., Hayes, T.L., Vaidya, H., Kanan, C.: Stream-51: streaming classification and novelty detection from videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)","DOI":"10.1109\/CVPRW50498.2020.00122"},{"key":"10_CR26","volume-title":"Greedy forward selection in the informative vector machine","author":"M Seeger","year":"2004","unstructured":"Seeger, M.: Greedy forward selection in the informative vector machine. Technical report, University of California at Berkeley, Tech. rep. (2004)"},{"issue":"1","key":"10_CR27","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1145\/3147.3165","volume":"11","author":"JS Vitter","year":"1985","unstructured":"Vitter, J.S.: Random sampling with a reservoir. ACM Trans. Math. Softw. (TOMS) 11(1), 37\u201357 (1985)","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"key":"10_CR28","unstructured":"Wei, K., Iyer, R., Bilmes, J.: Submodularity in data subset selection and active learning. In: International Conference on Machine Learning, pp. 1954\u20131963 (2015)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86523-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:03:22Z","timestamp":1757455402000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86523-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865221","9783030865238"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86523-8_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"869","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":"210","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":"0","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":"24% - 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":"3-4","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":"3-9","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":"The conference was held online due to the COVID-19 pandemic.","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)"}}]}}