{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T04:15:57Z","timestamp":1781756157622,"version":"3.54.5"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031703775","type":"print"},{"value":"9783031703782","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70378-2_13","type":"book-chapter","created":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T09:02:05Z","timestamp":1725181325000},"page":"207-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unraveling Anomalies in\u00a0Time: Unsupervised Discovery and\u00a0Isolation of\u00a0Anomalous Behavior in\u00a0Bio-Regenerative Life Support System Telemetry"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2264-9495","authenticated-orcid":false,"given":"Ferdinand","family":"Rewicki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2219-3590","authenticated-orcid":false,"given":"Jakob","family":"Gawlikowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5413-2234","authenticated-orcid":false,"given":"Julia","family":"Niebling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3193-3300","authenticated-orcid":false,"given":"Joachim","family":"Denzler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Adkisson, M., Kimmell, J.C., Gupta, M., Abdelsalam, M.: Autoencoder-based anomaly detection in smart farming ecosystem. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 3390\u20133399 (2021)","DOI":"10.1109\/BigData52589.2021.9671613"},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"107279","DOI":"10.1016\/j.compag.2022.107279","volume":"202","author":"AR de Araujo Zanella","year":"2022","unstructured":"de Araujo Zanella, A.R., et al.: CEIFA: a multi-level anomaly detector for smart farming. Comput. Electron. Agric. 202, 107279 (2022)","journal-title":"Comput. Electron. Agric."},{"key":"13_CR3","unstructured":"Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: SODA 2007, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027\u20131035. Society for Industrial and Applied Mathematics, USA (2007)"},{"key":"13_CR4","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TPAMI.2018.2823766","volume":"41","author":"B Barz","year":"2018","unstructured":"Barz, B., et al.: Detecting regions of maximal divergence for spatio-temporal anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1088\u20131101 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR5","unstructured":"Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Aaaiws 1994, Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 359\u2013370. AAAI Press (1994)"},{"issue":"1","key":"13_CR6","first-page":"50","volume":"4","author":"J Ca\u00f1adas","year":"2017","unstructured":"Ca\u00f1adas, J., et al.: Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes. Inf. Process. Agric. 4(1), 50\u201363 (2017)","journal-title":"Inf. Process. Agric."},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Choi, K., et\u00a0al.: Classification of growth conditions in paprika leaf using deep neural network and hyperspectral images. In: 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 93\u201395 (2021)","DOI":"10.1109\/ICUFN49451.2021.9528658"},{"issue":"5","key":"13_CR8","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster, A., et al.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Disc. 34(5), 1454\u20131495 (2020)","journal-title":"Data Min. Knowl. Disc."},{"key":"13_CR9","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/978-981-19-4687-5_32","volume-title":"Data, Engineering and Applications","author":"MM Joaquim","year":"2022","unstructured":"Joaquim, M.M., et al.: IoT and machine learning based anomaly detection in WSN for a smart greenhouse. In: Sharma, S., Peng, S.L., Agrawal, J., Shukla, R.K., Le, D.N. (eds.) Data, Engineering and Applications, pp. 421\u2013431. Springer Nature Singapore, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-19-4687-5_32"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Laptev, N., et\u00a0al.: 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. Association for Computing Machinery (2015)","DOI":"10.1145\/2783258.2788611"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Lu, Y., et\u00a0al.: Matrix profile xxiv: scaling time series anomaly detection to trillions of datapoints and ultra-fast arriving data streams. In: Kdd 2022, Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1173\u20131182. Association for Computing Machinery, New York, NY, USA (2022)","DOI":"10.1145\/3534678.3539271"},{"issue":"6","key":"13_CR12","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1007\/s10618-019-00647-x","volume":"33","author":"CH Lubba","year":"2019","unstructured":"Lubba, C.H., et al.: catch22: canonical time-series characteristics: selected through highly comparative time-series analysis. Data Min. Knowl. Discov. 33(6), 1821\u20131852 (2019)","journal-title":"Data Min. Knowl. Discov."},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"MacGregor, J.F., Kourti, T.: Statistical process control of multivariate processes. Control Eng. Pract. 27(2), 427\u2013437 (1994). iFAC Symposium on Advanced Control of Chemical Processes, Kyoto, Japan, 25-27 May 1994","DOI":"10.1016\/S1474-6670(17)48188-2"},{"key":"13_CR14","unstructured":"MacQueen, J., et\u00a0al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.\u00a01, pp. 281\u2013297. Oakland, CA, USA (1967)"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Nakamura, T., et\u00a0al.: Merlin: parameter-free discovery of arbitrary length anomalies in massive time series archives. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 1190\u20131195. IEEE, Sorrento, Italy (2020)","DOI":"10.1109\/ICDM50108.2020.00147"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.3390\/app13031778","volume":"13","author":"F Rewicki","year":"2023","unstructured":"Rewicki, F., et al.: Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series. Appl. Sci. 13, 1778 (2023)","journal-title":"Appl. Sci."},{"key":"13_CR17","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.11485183","author":"F Rewicki","year":"2024","unstructured":"Rewicki, F., et al.: EDEN ISS 2020 telemetry dataset. Zenodo (2024). https:\/\/doi.org\/10.5281\/zenodo.11485183","journal-title":"Zenodo"},{"key":"13_CR18","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comp. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comp. Appl. Math."},{"issue":"2","key":"13_CR19","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s10618-020-00727-3","volume":"35","author":"AP Ruiz","year":"2021","unstructured":"Ruiz, A.P., et al.: The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 35(2), 401\u2013449 (2021)","journal-title":"Data Min. Knowl. Disc."},{"issue":"1","key":"13_CR20","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1057\/s41599-020-0484-6","volume":"6","author":"T Sitthiyot","year":"2020","unstructured":"Sitthiyot, T., Holasut, K.: A simple method for measuring inequality. Palgrave Commun. 6(1), 112 (2020)","journal-title":"Palgrave Commun."},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Sohn, K., et\u00a0al.: Anomaly clustering: grouping images into coherent clusters of anomaly types. In: 2023 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 5468\u20135479 (2023)","DOI":"10.1109\/WACV56688.2023.00544"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Tafazoli, S., et\u00a0al.: Matrix profile xxix: C 22 MP, fusing catch 22 and the matrix profile to produce an efficient and interpretable anomaly detector. In: 2023 IEEE International Conference on Data Mining (ICDM), pp. 568\u2013577. IEEE (2023)","DOI":"10.1109\/ICDM58522.2023.00066"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Xhimitiku, I., et\u00a0al.: Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning. In: 2021 IEEE Int. Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 86\u201391 (2021)","DOI":"10.1109\/MetroAgriFor52389.2021.9628481"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70378-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T09:04:52Z","timestamp":1725181492000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70378-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703775","9783031703782"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70378-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}