{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:35:12Z","timestamp":1743021312727,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031306365"},{"type":"electronic","value":"9783031306372"}],"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\/978-3-031-30637-2_17","type":"book-chapter","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T10:08:13Z","timestamp":1681380493000},"page":"253-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SNN-AAD: Active Anomaly Detection Method for\u00a0Multivariate Time Series with\u00a0Sparse Neural Network"],"prefix":"10.1007","author":[{"given":"Xiaoou","family":"Ding","sequence":"first","affiliation":[]},{"given":"Yida","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hongzhi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Donghua","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yichen","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 16\u201318 May 2000, Dallas, Texas, USA, pp. 93\u2013104. ACM (2000)","key":"17_CR1","DOI":"10.1145\/335191.335388"},{"doi-asserted-by":"crossref","unstructured":"Chai, C., Cao, L., Li, G., Li, J., Luo, Y., Madden, S.: Human-in-the-loop outlier detection. In: Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference, 14\u201319 June 2020, pp. 19\u201333. ACM (2020)","key":"17_CR2","DOI":"10.1145\/3318464.3389772"},{"issue":"2","key":"17_CR3","first-page":"78","volume":"39","author":"T Dasu","year":"2016","unstructured":"Dasu, T., Duan, R., Srivastava, D.: Data quality for temporal streams. IEEE Data Eng. Bull. 39(2), 78\u201392 (2016)","journal-title":"IEEE Data Eng. Bull."},{"doi-asserted-by":"crossref","unstructured":"Dasu, T., Loh, J.M., Srivastava, D.: Empirical glitch explanations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA, 24\u201327 August 2014, pp. 572\u2013581 (2014)","key":"17_CR4","DOI":"10.1145\/2623330.2623716"},{"issue":"6","key":"17_CR5","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"HI Fawaz","year":"2020","unstructured":"Fawaz, H.I., et al.: InceptionTime: finding AlexNet for time series classification. Data Min. Knowl. Discov. 34(6), 1936\u20131962 (2020)","journal-title":"Data Min. Knowl. Discov."},{"unstructured":"Frankle, J., Carbin, M.: The lottery ticket hypothesis: finding sparse, trainable neural networks. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019)","key":"17_CR6"},{"doi-asserted-by":"crossref","unstructured":"Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier Detection for Temporal Data. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool Publishers, San Rafael (2014)","key":"17_CR7","DOI":"10.1007\/978-3-031-01905-0"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 770\u2013778. IEEE Computer Society (2016)","key":"17_CR8","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Jiang, J., Cui, B., Zhang, C., Fu, F.: DimBoost: boosting gradient boosting decision tree to higher dimensions. In: Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference, pp. 1363\u20131376. ACM (2018)","key":"17_CR9","DOI":"10.1145\/3183713.3196892"},{"doi-asserted-by":"crossref","unstructured":"Le, K., Papotti, P.: User-driven error detection for time series with events. In: 36th IEEE International Conference on Data Engineering, ICDE 2020, Dallas, TX, USA, 20\u201324 April 2020, pp. 745\u2013757. IEEE (2020)","key":"17_CR10","DOI":"10.1109\/ICDE48307.2020.00070"},{"doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6(1), 3:1\u20133:39 (2012)","key":"17_CR11","DOI":"10.1145\/2133360.2133363"},{"key":"17_CR12","doi-asserted-by":"publisher","first-page":"108194","DOI":"10.1109\/ACCESS.2022.3213038","volume":"10","author":"A Lundstr\u00f6m","year":"2022","unstructured":"Lundstr\u00f6m, A., O\u2019Nils, M., Qureshi, F.Z., Jantsch, A.: Improving deep learning based anomaly detection on multivariate time series through separated anomaly scoring. IEEE Access 10, 108194\u2013108204 (2022)","journal-title":"IEEE Access"},{"issue":"2","key":"17_CR13","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MCI.2013.2247823","volume":"8","author":"HP Mart\u00ednez","year":"2013","unstructured":"Mart\u00ednez, H.P., Bengio, Y., Yannakakis, G.N.: Learning deep physiological models of affect. IEEE Comput. Intell. Mag. 8(2), 20\u201333 (2013)","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"1","key":"17_CR14","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"FJO Morales","year":"2016","unstructured":"Morales, F.J.O., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)","journal-title":"Sensors"},{"doi-asserted-by":"crossref","unstructured":"Munawar, A., Vinayavekhin, P., Magistris, G.D.: Limiting the reconstruction capability of generative neural network using negative learning. In: 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP, pp. 1\u20136. IEEE (2017)","key":"17_CR15","DOI":"10.1109\/MLSP.2017.8168155"},{"unstructured":"Qin, X., Cao, L., Rundensteiner, E.A., Madden, S.: Scalable kernel density estimation-based local outlier detection over large data streams. In: Advances in Database Technology - 22nd International Conference on Extending Database Technology, EDBT 2019, Lisbon, Portugal, 26\u201329 March 2019, pp. 421\u2013432. OpenProceedings.org (2019)","key":"17_CR16"},{"issue":"4","key":"17_CR17","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1109\/TKDE.2006.1599387","volume":"18","author":"J Takeuchi","year":"2006","unstructured":"Takeuchi, J., Yamanishi, K.: A unifying framework for detecting outliers and change points from time series. IEEE Trans. Knowl. Data Eng. 18(4), 482\u2013492 (2006)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"unstructured":"Toledano, M., Cohen, I., Ben-Simhon, Y., Tadeski, I.: Real-time anomaly detection system for time series at scale. In: Proceedings of the KDD Workshop on Anomaly Detection, pp. 56\u201365 (2017)","key":"17_CR18"},{"doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, P., Xu, Y., He, Z.: Active-MTSAD: multivariate time series anomaly detection with active learning. In: 52nd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks, DSN 2022, Baltimore, MD, USA, 27\u201330 June 2022, pp. 263\u2013274. IEEE (2022)","key":"17_CR19","DOI":"10.1109\/DSN53405.2022.00036"},{"key":"17_CR20","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.1109\/ACCESS.2019.2962152","volume":"8","author":"X Wang","year":"2020","unstructured":"Wang, X., Wang, C.: Time series data cleaning: a survey. IEEE Access 8, 1866\u20131881 (2020)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30637-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:11:19Z","timestamp":1710263479000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30637-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306365","9783031306372"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30637-2_17","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":"14 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","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":"652","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":"125","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":"66","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":"19% - 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":"7.3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}