{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:00:24Z","timestamp":1742983224285,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030602895"},{"type":"electronic","value":"9783030602901"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-60290-1_12","type":"book-chapter","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T21:02:30Z","timestamp":1602622950000},"page":"156-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Learning-Based Anomaly Detection on KPI Data"],"prefix":"10.1007","author":[{"given":"Yongqin","family":"Huang","sequence":"first","affiliation":[]},{"given":"Yijie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Y., Mahajan, R., Sridharan, B.: A provider-side view of web search response time. In: ACM International Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, pp. 243\u2013254. ACM (2013)","DOI":"10.1145\/2534169.2486035"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Zhao, N., Zhu, J., Liu, R.: Label-Less: a semi-automatic labelling tool for KPI anomalies. In: IEEE International Conference on Computer Communications, pp. 1882\u20131890. IEEE (2019)","DOI":"10.1109\/INFOCOM.2019.8737429"},{"issue":"1","key":"12_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00401706.1990.10484583","volume":"32","author":"JM Lucas","year":"1990","unstructured":"Lucas, J.M., Saccucci, M.S.: Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32(1), 1\u201312 (1990)","journal-title":"Technometrics"},{"issue":"1","key":"12_CR4","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland, R.B., Cleveland, W.S., McRae, J.E.: STL: a seasonal-trend decomposition procedure based on loess. J. Off. Stat. 6(1), 3\u201373 (1990)","journal-title":"J. Off. Stat."},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Xu, H., Chen, W., Zhao, N.: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. In: Proceedings of the 2018 World Wide Web Conference, pp. 187\u2013196. ACM (2018)","DOI":"10.1145\/3178876.3185996"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Kieu, T., Yang, B., Guo, C.: Outlier detection for time series with recurrent autoencoder ensembles. In: 28th International Joint Conference on Artificial Intelligence, pp. 2725\u20132732. ACM (2019)","DOI":"10.24963\/ijcai.2019\/378"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Xu, H., Wang, Y., Wu, Z., Wang, Y.J: Embedding-based complex feature value coupling learning for detecting outliers in non-IID categorical data. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5541\u20135548. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.33015541"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Proceedings of the 2008 8th IEEE International Conference on Data Mining, pp. 413\u2013422. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Xu, H., Wang, Y., Wang, Y., Wu, Z.: MIX: a joint learning framework for detecting both clustered and scattered outliers in mixed-type data. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 1408\u20131413. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00182"},{"key":"12_CR10","unstructured":"Hochenbaum, J., Vallis, O.S., Kejariwal, A.: Automatic anomaly detection in the cloud via statistical learning. arXiv preprint arXiv:1704.07706 (2017)"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Yan, H., Flavel, A., Ge, Z.: Argus: end-to-end service anomaly detection and localization from an ISP\u2019s point of view. In: IEEE International Conference on Computer Communications, pp. 2756\u20132760. IEEE (2012)","DOI":"10.1109\/INFCOM.2012.6195694"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Amer, M., Goldstein, M., Abdennadher, S.: Enhancing one-class support vector machines for unsupervised anomaly detection. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, pp. 8\u201315. ACM (2013)","DOI":"10.1145\/2500853.2500857"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T.: LOF: identifying density-based local outliers. In: International Conference on Management of Data, pp. 93\u2013104. ACM (2000)","DOI":"10.1145\/335191.335388"},{"key":"12_CR14","unstructured":"Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 359\u2013370. AAAI Press (1994)"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., Campana, B., Mueen, A.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 262\u2013270. ACM (2012)","DOI":"10.1145\/2339530.2339576"},{"key":"12_CR16","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","volume":"74","author":"WS Cleveland","year":"1979","unstructured":"Cleveland, W.S.: Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829\u2013836 (1979)","journal-title":"J. Am. Stat. Assoc."},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Fontugne, R., Borgnat, P., Abry, P.: MAWILab: Combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking. In: Proceedings of the 6th International Conference, pp. 1\u201312. ACM (2010)","DOI":"10.1145\/1921168.1921179"},{"issue":"1","key":"12_CR18","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MNET.2009.4804320","volume":"23","author":"S Shanbhag","year":"2009","unstructured":"Shanbhag, S., Wolf, T.: Accurate anomaly detection through parallelism. IEEE Netw. 23(1), 22\u201328 (2009)","journal-title":"IEEE Netw."},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Paparrizos, J., Gravano, L.: k-Shape: efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1855\u20131870. ACM (2015)","DOI":"10.1145\/2723372.2737793"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Liu, D., Zhao, Y., Xu, H.: Opprentice: towards practical and automatic anomaly detection through machine learning. In: Proceedings of the 2015 Internet Measurement Conference, pp. 211\u2013224. ACM (2015)","DOI":"10.1145\/2815675.2815679"},{"key":"12_CR21","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, pp. 1939\u20131947. ACM (2015)","DOI":"10.1145\/2783258.2788611"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60290-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T08:17:43Z","timestamp":1619252263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60290-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030602895","9783030602901"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60290-1_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/apwebwaim2020\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"259","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":"68","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":"37","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":"26% - 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":"4.6","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was organized as a fully online conference.","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)"}}]}}