{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:12:12Z","timestamp":1777428732114,"version":"3.51.4"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T00:00:00Z","timestamp":1614729600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T00:00:00Z","timestamp":1614729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["The VLDB Journal"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s00778-021-00655-8","type":"journal-article","created":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T17:06:29Z","timestamp":1614791189000},"page":"909-931","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Unsupervised and scalable subsequence anomaly detection in large data series"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8516-0123","authenticated-orcid":false,"given":"Paul","family":"Boniol","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michele","family":"Linardi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Federico","family":"Roncallo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Meftah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel","family":"Remy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,3]]},"reference":[{"key":"655_CR1","unstructured":"http:\/\/data-acoustics.com\/measurements\/bearing-faults\/bearing-4\/ (2007)"},{"key":"655_CR2","unstructured":"http:\/\/www.nyc.gov\/html\/tlc\/html\/about\/trip_record_data.shtml (2015)"},{"key":"655_CR3","unstructured":"http:\/\/helios.mi.parisdescartes.fr\/~themisp\/norma\/"},{"key":"655_CR4","doi-asserted-by":"crossref","unstructured":"Abboud, D., Elbadaoui, M., Smith, W., Randall, R.: Advanced bearing diagnostics: A comparative study of two powerful approaches. MSSP 114 (2019)","DOI":"10.1016\/j.ymssp.2018.05.011"},{"key":"655_CR5","doi-asserted-by":"crossref","unstructured":"Abdul-Aziz, A., Woike, M.R., Oza, N.C., Matthews, B.L., lekki, J.D.: Rotor health monitoring combining spin tests and data-driven anomaly detection methods. Struct. Health Monit. (2012)","DOI":"10.1177\/1475921710395811"},{"key":"655_CR6","doi-asserted-by":"crossref","unstructured":"Ahmad, S., Lavin, A., Purdy, S., Agha, Z.: Unsupervised real-time anomaly detection for streaming data. Neurocomputing (2017)","DOI":"10.1016\/j.neucom.2017.04.070"},{"key":"655_CR7","doi-asserted-by":"crossref","unstructured":"Antoni, J., Borghesani, P.: A statistical methodology for the design of condition indicators. Mech. Syst. Signal Process. 290\u2013327 (2019)","DOI":"10.1016\/j.ymssp.2018.05.012"},{"key":"655_CR8","unstructured":"Bagnall, A.J., Cole, R.L., Palpanas, T., Zoumpatianos, K.: Data series management (dagstuhl seminar 19282). Dagstuhl Rep. 9(7), 24\u201339 (2019)"},{"key":"655_CR9","unstructured":"Barnet, V., Lewis, T.: Outliers in Statistical Data. Wiley, New York (1994)"},{"key":"655_CR10","doi-asserted-by":"crossref","unstructured":"Boniol, P., Linardi, M., Roncallo, F., Palpanas, T.: Automated Anomaly Detection in Large Sequences. In: ICDE pp. 1834\u20131837 (2020)","DOI":"10.1109\/ICDE48307.2020.00182"},{"key":"655_CR11","doi-asserted-by":"crossref","unstructured":"Boniol, P., Linardi, M., Roncallo, F., Palpanas, T.: SAD: an unsupervised system for subsequence anomaly detection. In: 36th IEEE International Conference on Data Engineering, ICDE, pp. 1778\u20131781. IEEE (2020)","DOI":"10.1109\/ICDE48307.2020.00168"},{"issue":"11","key":"655_CR12","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.14778\/3407790.3407792","volume":"13","author":"P Boniol","year":"2020","unstructured":"Boniol, P., Palpanas, T.: Series2graph: graph-based subsequence anomaly detection for time series. Proc. VLDB Endow. 13(11), 1821\u20131834 (2020)","journal-title":"Proc. VLDB Endow."},{"issue":"12","key":"655_CR13","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.14778\/3415478.3415514","volume":"13","author":"P Boniol","year":"2020","unstructured":"Boniol, P., Palpanas, T., Meftah, M., Remy, E.: Graphan: graph-based subsequence anomaly detection. Proc. VLDB Endow. 13(12), 2941\u20132944 (2020)","journal-title":"Proc. VLDB Endow."},{"key":"655_CR14","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: Identifying density-based local outliers. In: SIGMOD (2000)","DOI":"10.1145\/342009.335388"},{"key":"655_CR15","doi-asserted-by":"crossref","unstructured":"Bryant, P.G.: On the minimum description length (mdl) principle for hierarchical classifications. In: Data Science, Classification, and Related Methods (1998)","DOI":"10.1007\/978-4-431-65950-1_17"},{"key":"655_CR16","doi-asserted-by":"publisher","unstructured":"Bu, Y., Chen, L., Fu, A.W.C., Liu, D.: Efficient anomaly monitoring over moving object trajectory streams. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201909, pp. 159\u2013168. Association for Computing Machinery, New York, NY, USA (2009). https:\/\/doi.org\/10.1145\/1557019.1557043","DOI":"10.1145\/1557019.1557043"},{"key":"655_CR17","doi-asserted-by":"crossref","unstructured":"Bu, Y., Leung, O.T., Fu, A.W., Keogh, E.J., Pei, J., Meshkin, S.: WAT: finding top-k discords in time series database. In: SIAM (2007)","DOI":"10.1137\/1.9781611972771.43"},{"key":"655_CR18","doi-asserted-by":"crossref","unstructured":"Chiu, B.Y., Keogh, E.J., Lonardi, S.: Probabilistic discovery of time series motifs. In: KDD (2003)","DOI":"10.1145\/956750.956808"},{"key":"655_CR19","doi-asserted-by":"crossref","unstructured":"Echihabi, K., Zoumpatianos, K., Palpanas, T., Benbrahim, H.: The lernaean hydra of data series similarity search: an experimental evaluation of the state of the art. PVLDB 2, 112\u2013127 (2018)","DOI":"10.14778\/3282495.3282498"},{"key":"655_CR20","first-page":"402","volume":"13","author":"K Echihabi","year":"2019","unstructured":"Echihabi, K., Zoumpatianos, K., Palpanas, T., Benbrahim, H.: Return of the lernaean hydra: experimental evaluation of data series approximate similarity search. PVLDB 13, 402\u2013419 (2019)","journal-title":"PVLDB"},{"key":"655_CR21","doi-asserted-by":"crossref","unstructured":"Fu, A.W., Leung, O.T., Keogh, E.J., Lin, J.: Finding time series discords based on haar transform. In: ADMA pp. 31\u201341 (2006)","DOI":"10.1007\/11811305_3"},{"issue":"1","key":"655_CR22","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s10618-018-0589-3","volume":"33","author":"S Gharghabi","year":"2019","unstructured":"Gharghabi, S., Yeh, C.M., Ding, Y., Ding, W., Hibbing, P., LaMunion, S., Kaplan, A., Crouter, S.E., Keogh, E.J.: Domain agnostic online semantic segmentation for multi-dimensional time series. Data Min. Knowl. Discov. 33(1), 96\u2013130 (2019)","journal-title":"Data Min. Knowl. Discov."},{"key":"655_CR23","doi-asserted-by":"publisher","unstructured":"Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000 (June 13)). Circulation Electronic Pages: http:\/\/circ.ahajournals.org\/content\/101\/23\/e215.fullPMID:1085218; https:\/\/doi.org\/10.1161\/01.CIR.101.23.e215","DOI":"10.1161\/01.CIR.101.23.e215"},{"issue":"1","key":"655_CR24","doi-asserted-by":"publisher","first-page":"6:1","DOI":"10.1145\/2940329","volume":"11","author":"J Grabocka","year":"2016","unstructured":"Grabocka, J., Schilling, N., Schmidt-Thieme, L.: Latent time-series motifs. TKDD 11(1), 6:1-6:20 (2016)","journal-title":"TKDD"},{"key":"655_CR25","doi-asserted-by":"crossref","unstructured":"Hadjem, M., Na\u00eft-Abdesselam, F., Khokhar, A.A.: St-segment and t-wave anomalies prediction in an ECG data using rusboost. In: Healthcom (2016)","DOI":"10.1109\/HealthCom.2016.7749493"},{"key":"655_CR26","doi-asserted-by":"crossref","unstructured":"Keogh, E., Lin, J.: Clustering of time-series subsequences is meaningless: implications for previous and future research. KAIS 8(2) (2004)","DOI":"10.1007\/s10115-004-0172-7"},{"key":"655_CR27","first-page":"99","volume":"14","author":"E Keogh","year":"2007","unstructured":"Keogh, E., Lonardi, S., Ratanamahatana, C., Wei, L., Lee, S.H., Handley, J.: Compression-based data mining of sequential data. DMKD 14, 99\u2013129 (2007)","journal-title":"DMKD"},{"key":"655_CR28","unstructured":"Keogh, E.J., Lin, J., Fu, A.W.: HOT SAX: efficiently finding the most unusual time series subsequence. In: ICDM (2005)"},{"key":"655_CR29","doi-asserted-by":"crossref","unstructured":"Kondylakis, H., Dayan, N., Zoumpatianos, K., Palpanas, T.: Coconut: sortable summarizations for scalable indexes over static and streaming data series. VLDBJ 28(6) (2019)","DOI":"10.1007\/s00778-019-00573-w"},{"key":"655_CR30","doi-asserted-by":"crossref","unstructured":"Lee, J., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: 2008 IEEE 24th International Conference on Data Engineering, pp. 140\u2013149 (2008)","DOI":"10.1109\/ICDE.2008.4497422"},{"key":"655_CR31","unstructured":"Lee, T., Gottschlich, J., Tatbul, N., Metcalf, E., Zdonik, S.: greenhouse: a zero-positive machine learning system for time-series anomaly detection. CoRR arXiv:abs\/1801.03168 (2018). URL http:\/\/arxiv.org\/abs\/1801.03168"},{"key":"655_CR32","doi-asserted-by":"crossref","unstructured":"Li, X., Lin, J.: Linear time motif discovery in time series. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 136\u2013144. SIAM (2019)","DOI":"10.1137\/1.9781611975673.16"},{"key":"655_CR33","first-page":"2236","volume":"11","author":"M Linardi","year":"2019","unstructured":"Linardi, M., Palpanas, T.: Scalable, variable-length similarity search in data series: the ulisse approach. PVLDB 11, 2236\u20132248 (2019)","journal-title":"PVLDB"},{"key":"655_CR34","doi-asserted-by":"crossref","unstructured":"Linardi, M., Zhu, Y., Palpanas, T., Keogh, E.: Matrix profile x: Valmod - scalable discovery of variable-length motifs in data series. In: SIGMOD (2018)","DOI":"10.1145\/3183713.3183744"},{"key":"655_CR35","doi-asserted-by":"crossref","unstructured":"Linardi, M., Zhu, Y., Palpanas, T., Keogh, E.J.: Matrix Profile Goes MAD: variable-length motif and discord discovery in data series. In: DAMI (2020)","DOI":"10.1007\/s10618-020-00685-w"},{"key":"655_CR36","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: ICDM, ICDM (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"655_CR37","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, X., Wang, F.: Efficient detection of discords for time series stream. In: Advances in Data and Web Management (2009)","DOI":"10.1007\/978-3-642-00672-2_62"},{"key":"655_CR38","doi-asserted-by":"crossref","unstructured":"Luo, W., Gallagher, M.: Faster and parameter-free discord search in quasi-periodic time series. In: Advances in Knowledge Discovery and Data Mining (2011)","DOI":"10.1007\/978-3-642-20847-8_12"},{"key":"655_CR39","unstructured":"Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: ESANN (2015)"},{"key":"655_CR40","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45\u201350 (2001)","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"655_CR41","doi-asserted-by":"crossref","unstructured":"Mueen, A., Keogh, E.J., Zhu, Q., Cash, S., Westover, M.B.: Exact discovery of time series motifs. In: SDM (2009)","DOI":"10.1137\/1.9781611972795.41"},{"issue":"2","key":"655_CR42","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1145\/2814710.2814719","volume":"44","author":"T Palpanas","year":"2015","unstructured":"Palpanas, T.: Data series management: the road to big sequence analytics. SIGMOD Rec. 44(2), 47\u201352 (2015)","journal-title":"SIGMOD Rec."},{"key":"655_CR43","doi-asserted-by":"crossref","unstructured":"Palpanas, T.: Evolution of a Data Series Index. In: CCIS, pp. 68\u201383 (2020)","DOI":"10.1007\/978-3-030-44900-1_5"},{"key":"655_CR44","doi-asserted-by":"crossref","unstructured":"Palpanas, T., Beckmann, V.: Report on the first and second interdisciplinary time series analysis workshop (ITISA). SIGREC 48(3) (2019)","DOI":"10.1145\/3377391.3377400"},{"issue":"1","key":"655_CR45","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1145\/2949741.2949758","volume":"45","author":"J Paparrizos","year":"2016","unstructured":"Paparrizos, J., Gravano, L.: K-shape: efficient and accurate clustering of time series. SIGMOD Rec. 45(1), 69\u201376 (2016). https:\/\/doi.org\/10.1145\/2949741.2949758","journal-title":"SIGMOD Rec."},{"key":"655_CR46","unstructured":"Paul Boniol (advisor: Themis Palpanas): Unsupervised subsequence anomaly detection in large sequences. In: Proceedings of the VLDB 2020 PhD Workshop colocated with the 46th International Conference on Very Large Databases (VLDB 2020), CEUR Workshop Proceedings, vol. 2652 (2020)"},{"key":"655_CR47","doi-asserted-by":"crossref","unstructured":"Peng, B., Palpanas, T., Fatourou, P.: Messi: In-memory data series indexing. In: ICDE (2020)","DOI":"10.1109\/ICDE48307.2020.00036"},{"key":"655_CR48","doi-asserted-by":"crossref","unstructured":"Peng, B., Palpanas, T., Fatourou, P.: Paris+: data series indexing on multi-core architectures. In: TKDE (2020)","DOI":"10.1109\/TKDE.2020.2975180"},{"key":"655_CR49","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., Keogh, E.J., Lonardi, S., Evans, S.: Time series epenthesis: clustering time series streams requires ignoring some data. In: 2011 IEEE 11th International Conference on Data Mining, pp. 547\u2013556 (2011)","DOI":"10.1109\/ICDM.2011.146"},{"key":"655_CR50","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","volume":"14","author":"J Rissanen","year":"1978","unstructured":"Rissanen, J.: Modeling by shortest data description. Automatica 14, 465\u2013471 (1978)","journal-title":"Automatica"},{"key":"655_CR51","unstructured":"Safran: Personal communication with Dr. Dohy Hong (2018)"},{"key":"655_CR52","unstructured":"Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S.: Time series anomaly discovery with grammar-based compression. In: EDBT (2015)"},{"key":"655_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3051126","volume":"12","author":"P Senin","year":"2018","unstructured":"Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S.: Grammarviz 3.0: Interactive discovery of variable-length time series patterns. TKDD 12, 1\u201328 (2018)","journal-title":"TKDD"},{"key":"655_CR54","first-page":"24","volume":"19","author":"J Shieh","year":"2009","unstructured":"Shieh, J., Keogh, E.: iSAX: disk-aware mining and indexing of massive time series datasets. DMKD 19, 24\u201327 (2009)","journal-title":"DMKD"},{"key":"655_CR55","unstructured":"Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Online outlier detection in sensor data using non-parametric models. In: VLDB (2006)"},{"key":"655_CR56","unstructured":"Wang, J., Balasubramanian, A., de\u00a0la Vega, L.M., Green, J., Samal, A., Prabhakaran, B.: Word recognition from continuous articulatory movement time-series data using symbolic representations. In: SLPAT (2013)"},{"key":"655_CR57","doi-asserted-by":"crossref","unstructured":"Wang, X., Lin, J., Patel, N., Braun, M.: A self-learning and online algorithm for time series anomaly detection, with application in CPU manufacturing. In: CIKM (2016)","DOI":"10.1145\/2983323.2983344"},{"key":"655_CR58","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1093\/sleep\/21.7.749","volume":"21","author":"C Whitney","year":"1998","unstructured":"Whitney, C., Gottlieb, D., Redline, S., Norman, R., Dodge, R., Shahar, E., Surovec, S., Nieto, F.: Reliability of scoring respiratory disturbance indices and sleep staging. Sleep 21, 749\u2013757 (1998)","journal-title":"Sleep"},{"key":"655_CR59","doi-asserted-by":"crossref","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80\u201383 (1945). http:\/\/www.jstor.org\/stable\/3001968","DOI":"10.2307\/3001968"},{"key":"655_CR60","doi-asserted-by":"crossref","unstructured":"Wu, Q., Qi, X., Fuller, E., Zhang, C.Q.: Follow the leader: A centrality guided clustering and its application to social network analysis. Sci. World J. (2013)","DOI":"10.1155\/2013\/368568"},{"key":"655_CR61","doi-asserted-by":"crossref","unstructured":"Yankov, D., Keogh, E., Rebbapragada, U.: Disk aware discord discovery: finding unusual time series in terabyte sized datasets. In: ICDM (2007)","DOI":"10.1109\/ICDM.2007.61"},{"key":"655_CR62","doi-asserted-by":"crossref","unstructured":"Yankov, D., Keogh, E., Rebbapragada, U.: Disk aware discord discovery: finding unusual time series in terabyte sized datasets. KAIS 17(2) (2008)","DOI":"10.1007\/s10115-008-0131-9"},{"key":"655_CR63","doi-asserted-by":"crossref","unstructured":"Yankov, D., Keogh, E.J., Medina, J., Chiu, B.Y., Zordan, V.B.: Detecting time series motifs under uniform scaling. In: KDD (2007)","DOI":"10.1145\/1281192.1281282"},{"key":"655_CR64","doi-asserted-by":"crossref","unstructured":"Yeh, C., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H., Silva, D., Mueen, A., Keogh, E.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: ICDM (2016)","DOI":"10.1109\/ICDM.2016.0179"},{"key":"655_CR65","doi-asserted-by":"crossref","unstructured":"Yu, Y., Cao, L., Rundensteiner, E.A., Wang, Q.: Outlier detection over massive-scale trajectory streams. ACM Trans. Database Syst. (TODS) 42, 1\u201333 (2017)","DOI":"10.1145\/3013527"},{"key":"655_CR66","doi-asserted-by":"publisher","unstructured":"Zhu, Y., Zimmerman, Z., Senobari, N.S., Yeh, C.M., Funning, G., Mueen, A., Brisk, P., Keogh, E.: Matrix profile ii: Exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 739\u2013748 (2016). https:\/\/doi.org\/10.1109\/ICDM.2016.0085","DOI":"10.1109\/ICDM.2016.0085"}],"updated-by":[{"DOI":"10.1007\/s00778-021-00678-1","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000}}],"container-title":["The VLDB Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-021-00655-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00778-021-00655-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-021-00655-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T10:56:14Z","timestamp":1634295374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00778-021-00655-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,3]]},"references-count":66,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["655"],"URL":"https:\/\/doi.org\/10.1007\/s00778-021-00655-8","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00778-021-00678-1","asserted-by":"object"}]},"ISSN":["1066-8888","0949-877X"],"issn-type":[{"value":"1066-8888","type":"print"},{"value":"0949-877X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,3]]},"assertion":[{"value":"23 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2021","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00778-021-00678-1","URL":"https:\/\/doi.org\/10.1007\/s00778-021-00678-1","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}