{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:10:18Z","timestamp":1766067018086,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030204846"},{"type":"electronic","value":"9783030204853"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20485-3_17","type":"book-chapter","created":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T23:09:30Z","timestamp":1560812970000},"page":"218-231","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Framework to Monitor Machine Learning Systems Using Concept Drift Detection"],"prefix":"10.1007","author":[{"given":"Xianzhe","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wally","family":"Lo Faro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoying","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravi Santosh","family":"Arvapally","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,18]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Arvapally, R.S., Hicsasmaz, H., Lo Faro, W.: Artificial intelligence applied to challenges in the fields of operations and customer support. In: 2017 IEEE International Conference on Big Data (IEEE Big Data), Boston, pp. 3562\u20133569 (2017)","DOI":"10.1109\/BigData.2017.8258347"},{"key":"17_CR2","first-page":"44:1","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama, J., Zliobaite, L., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. J. ACM Comput. Surv. (CSUR) 46, 44:1\u201344:37 (2014)","journal-title":"J. ACM Comput. Surv. (CSUR)"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Pastorello, G., et al.: Hunting data rouges at scale: data quality control for observational data in research infrastructures. In: 2017 IEEE 13th International Conference on e-Science (e-Science), Auckland, pp. 446\u2013447 (2017)","DOI":"10.1109\/eScience.2017.64"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Gamage, S., Premaratne, U.: Detecting and adapting to concept drift in continually evolving stochastic processes. In: ACM Proceedings of the International Conference on Big Data and Internet of Thing, London, pp. 109\u2013114 (2017)","DOI":"10.1145\/3175684.3175723"},{"issue":"5","key":"17_CR5","first-page":"1","volume":"32","author":"G Webb","year":"2018","unstructured":"Webb, G., Lee, L.K., Goethals, B., Petitjean, F.: Analyzing concept drift and shift from sample data. J. Data Min. Knowl. Discov. 32(5), 1\u201321 (2018)","journal-title":"J. Data Min. Knowl. Discov."},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Gholipur, A., Hosseini, M.J., Beigy, H.: An adaptive regression tree for non-stationary data streams. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing (ACM), Coimbra, pp. 815\u2013817 (2013)","DOI":"10.1145\/2480362.2480519"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Jadhav, A., Deshpande, L.: An efficient approach to detect concept drifts in data streams. In: IEEE 7th International Advance Computing Conference (IEEE IACC), Hyderabad, pp. 28\u201332 (2017)","DOI":"10.1109\/IACC.2017.0021"},{"issue":"3","key":"17_CR8","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"5","key":"17_CR9","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1109\/TST.2016.7590319","volume":"21","author":"M Ding","year":"2016","unstructured":"Ding, M., Tian, H.: PCA-based network traffic anomaly detection. J. Tsinghua Sci. Technol. 21(5), 500\u2013509 (2016)","journal-title":"J. Tsinghua Sci. Technol."},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, L., Veitch, D., Kotagiri, R.: The role of KL divergence in anomaly detection. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, San Jose, pp. 123\u2013124 (2011)","DOI":"10.1145\/1993744.1993787"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Laptev, N., Amizadeh, S., Flint, I.: Generic and scalable framework for automated time-series anomaly detection. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM), Sydney, pp. 1939\u20131947 (2015)","DOI":"10.1145\/2783258.2788611"},{"key":"17_CR12","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR13","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, pp. 226\u2013231 (1996)"},{"issue":"1","key":"17_CR14","first-page":"3","volume":"6","author":"FT Liu","year":"2012","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation based anomaly detection. ACM Trans. Knowl. Discov. Data (TKDD) 6(1), 3 (2012)","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Liu, A., Zhang, G., Lu, J.: Fuzzy time windowing for gradual concept drift adaptation. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, pp. 1\u20136 (2017)","DOI":"10.1109\/FUZZ-IEEE.2017.8015596"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Geng, Y., Zhang, J.: An ensemble classifier algorithm for mining data streams based on concept drift. In: 10th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, pp. 227\u2013230 (2017)","DOI":"10.1109\/ISCID.2017.121"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Hu, H., Kantardzic, M.M., Lyu, L.: Detecting different types of concept drifts with ensemble framework. In: 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, pp. 344\u2013350 (2018)","DOI":"10.1109\/ICMLA.2018.00058"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Senaratne, H., Broring, A., Schreck, T., Lehle, D.: Moving on Twitter: using episodic hotspot and drift analysis to detect and characterise spatial trajectories: In: 7th ACM SIGSPATIAL International Workshop on Location - Based Social Networks (LBSN), Dallas (2014)","DOI":"10.1145\/2755492.2755497"}],"container-title":["Lecture Notes in Business Information Processing","Business Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20485-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:45:09Z","timestamp":1710251109000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-20485-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030204846","9783030204853"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20485-3_17","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"type":"print","value":"1865-1348"},{"type":"electronic","value":"1865-1356"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"18 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Business Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seville","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2019","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":"bis2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bis.ue.poznan.pl\/","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":"223","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":"67","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":"30% - 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":"2,5","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":"From the BIS workshops 57 papers were accepted from 139 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)"}}]}}