{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:16:16Z","timestamp":1775135776749,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"crossref","award":["298020"],"award-info":[{"award-number":["298020"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"crossref","award":["328058"],"award-info":[{"award-number":["328058"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"crossref"}]},{"name":"University of Oulu including Oulu University Hospital"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Autom Softw Eng"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Detecting anomalies in software logs has become a notable concern for software engineers and maintainers as they represent anomalies in software execution paths and states. This paper propose a novel anomaly detection approach based on the Siamese network on top of Recurrent Neural Networks(RNN). Accordingly, we introduce a novel training pair generation algorithm to train the Siamese network which reduces generated training significantly while maintaining the <jats:inline-formula><jats:alternatives><jats:tex-math>$$F_1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>F<\/mml:mi>\n                    <mml:mn>1<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> score. Additionally, we propose a hybrid model by combining the Siamese network with a traditional feedforward neural network to make end-to-end training possible, reducing engineering effort in setting up a deep-learning-based log anomaly detector. Furthermore, we provides validations of the approach on the Hadoop Distributed File System (HDFS), Blue Gene\/L (BGL), and Hadoop map-reduce task log datasets. To the best of our knowledge, the proposed approach outperforms other methods on the same dataset at the <jats:inline-formula><jats:alternatives><jats:tex-math>$$F_1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>F<\/mml:mi>\n                    <mml:mn>1<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> scores of respectively 0.99, 0.99, and 0.94 on HDFS, BGL, and Hadoop datasets, resulting in a new state-of-the-art performance.To further evaluate the proposed method, we examine our method\u2019s robustness to log evolutions by evaluating the model on synthetically evolved log sequences; we got the <jats:inline-formula><jats:alternatives><jats:tex-math>$$F_1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>F<\/mml:mi>\n                    <mml:mn>1<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> score of 0.95 on the HDFS dataset at the noise ratio of <jats:inline-formula><jats:alternatives><jats:tex-math>$$20\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>20<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Finally, we dive deep into some of the side benefits of the Siamese network. Accordingly, we introduce an unsupervised log evolution monitoring method alongside a visualization technique that facilitates model interpretability.<\/jats:p>","DOI":"10.1007\/s10515-022-00365-7","type":"journal-article","created":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T10:02:39Z","timestamp":1665655359000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["SiaLog: detecting anomalies in software execution logs using the siamese network"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6031-1765","authenticated-orcid":false,"given":"Shayan","family":"Hashemi","sequence":"first","affiliation":[]},{"given":"Mika","family":"M\u00e4ntyl\u00e4","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"issue":"4","key":"365_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"Herv\u00e9 Abdi","year":"2010","unstructured":"Abdi, Herv\u00e9, Williams, Lynne J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433\u2013459 (2010)","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"issue":"12","key":"365_CR2","doi-asserted-by":"publisher","first-page":"9321","DOI":"10.1007\/s00521-018-3844-z","volume":"31","author":"Kian Ahrabian","year":"2019","unstructured":"Ahrabian, Kian, BabaAli, Bagher: Usage of autoencoders and siamese networks for online handwritten signature verification. Neural Comput. Appl. 31(12), 9321\u20139334 (2019)","journal-title":"Neural Comput. Appl."},{"key":"365_CR3","doi-asserted-by":"crossref","unstructured":"Alhersh, T., Stuckenschmidt, H.: On the combination of imu and optical flow for action recognition. In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 17\u201321. IEEE, (2019)","DOI":"10.1109\/PERCOMW.2019.8730743"},{"key":"365_CR4","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprintarXiv:1409.0473, (2014)"},{"key":"365_CR5","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., Torr, P. H.: Fully-convolutional siamese networks for object tracking. In: European conference on computer vision, pages 850\u2013865. Springer, (2016)","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"365_CR6","doi-asserted-by":"crossref","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., Shah, R.: Signature verification using a \u201csiamese\u201d time delay neural network. In: Advances in neural information processing systems, pages 737\u2013744, (1994)","DOI":"10.1142\/9789812797926_0003"},{"key":"365_CR7","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: A survey. arXiv preprintarXiv:1901.03407, (2019)","DOI":"10.1145\/3394486.3406704"},{"key":"365_CR8","unstructured":"Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), volume\u00a01, pages 539\u2013546. IEEE, (2005)"},{"key":"365_CR9","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprintarXiv:1412.3555, (2014)"},{"key":"365_CR10","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprintarXiv:1810.04805, (2018)"},{"key":"365_CR11","unstructured":"Dey, S., Dutta, A., Toledo, J. I., Ghosh, S. K., Llad\u00f3s, J., Pal, U.: Signet: Convolutional siamese network for writer independent offline signature verification. arXiv preprintarXiv:1707.02131, (2017)"},{"key":"365_CR12","doi-asserted-by":"crossref","unstructured":"Du, ., Li, F., Zheng, G., Srikumar, V.: Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pages 1285\u20131298, (2017)","DOI":"10.1145\/3133956.3134015"},{"issue":"3","key":"365_CR13","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s10515-020-00277-4","volume":"27","author":"Geanderson Esteves","year":"2020","unstructured":"Esteves, Geanderson, Figueiredo, Eduardo, Veloso, Adriano, Viggiato, Markos, Ziviani, Nivio: Understanding machine learning software defect predictions. Autom. Softw. Eng. 27(3), 369\u2013392 (2020). https:\/\/doi.org\/10.1007\/s10515-020-00277-4. (ISSN 1573-7535)","journal-title":"Autom. Softw. Eng."},{"key":"365_CR14","doi-asserted-by":"crossref","unstructured":"Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic siamese network for visual object tracking. In: The IEEE International Conference on Computer Vision (ICCV), (2017)","DOI":"10.1109\/ICCV.2017.196"},{"key":"365_CR15","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), volume\u00a02, pages 1735\u20131742. IEEE, (2006)"},{"key":"365_CR16","doi-asserted-by":"publisher","unstructured":"He, P., Zhu, J., Zheng, Z., Lyu, M.\u00a0R.: Drain: An online log parsing approach with fixed depth tree. In: 2017 IEEE International Conference on Web Services (ICWS), pages 33\u201340, (2017). https:\/\/doi.org\/10.1109\/ICWS.2017.13","DOI":"10.1109\/ICWS.2017.13"},{"key":"365_CR17","unstructured":"He, S., Zhu, J., He, P., Lyu, M. R.: Loghub: A large collection of system log datasets towards automated log analytics, (2020)"},{"issue":"8","key":"365_CR18","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"Sepp Hochreiter","year":"1997","unstructured":"Hochreiter, Sepp, Schmidhuber, J\u00fcrgen.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"365_CR19","doi-asserted-by":"crossref","unstructured":"Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M. J.: Towards understanding action recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), (2013)","DOI":"10.1109\/ICCV.2013.396"},{"issue":"7553","key":"365_CR20","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Yann LeCun","year":"2015","unstructured":"LeCun, Yann, Bengio, Yoshua, Hinton, Geoffrey: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"365_CR21","unstructured":"LeCun, Yann et\u00a0al.: Lenet-5, convolutional neural networks. 20 (5):14, (2015b), http:\/\/yann.lecun.com\/exdb\/lenet"},{"issue":"2","key":"365_CR22","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/MS.2015.50","volume":"32","author":"Marko M\u00e4kinen Lepp\u00e4nen","year":"2015","unstructured":"Lepp\u00e4nen, Marko M\u00e4kinen., Simo, Pagels, Max, Eloranta, Veli-Pekka, Itkonen, Juha, M\u00e4ntyl\u00e4.: Mika V, M\u00e4nnist\u00f6, Tomi: The highways and country roads to continuous deployment. IEEE Software 32(2), 64\u201372 (2015)","journal-title":"IEEE Software"},{"issue":"1","key":"365_CR23","first-page":"6765","volume":"18","author":"Lisha Li","year":"2017","unstructured":"Li, Lisha, Jamieson, Kevin, DeSalvo, Giulia, Rostamizadeh, Afshin, Talwalkar, Ameet: Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18(1), 6765\u20136816 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"365_CR24","doi-asserted-by":"crossref","unstructured":"Lin, Q., Zhang, H., Lou, J., Zhang, Y., Chen, X.: Log clustering based problem identification for online service systems. In: 2016 IEEE\/ACM 38th International Conference on Software Engineering Companion (ICSE-C), pages 102\u2013111, (2016)","DOI":"10.1145\/2889160.2889232"},{"key":"365_CR25","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A. C.: Ssd: Single shot multibox detector. In: European conference on computer vision, pages 21\u201337. Springer, (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"365_CR26","unstructured":"Lou, J.-G., Fu, Q., Yang, S., Xu, Y., Li, J.: Mining invariants from console logs for system problem detection. In: USENIX Annual Technical Conference, pages 1\u201314, (2010)"},{"key":"365_CR27","doi-asserted-by":"crossref","unstructured":"Lu, S., Wei, X., Li, Y., Wang, L.: Detecting anomaly in big data system logs using convolutional neural network. In: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), pages 151\u2013158. IEEE, (2018)","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00037"},{"key":"365_CR29","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprintarXiv:1802.03426, (2018)","DOI":"10.21105\/joss.00861"},{"key":"365_CR30","doi-asserted-by":"crossref","unstructured":"Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., Chen, Y., Zhang, R., Tao, S., Sun, P., et\u00a0al.: Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, volume\u00a07, pages 4739\u20134745, (2019)","DOI":"10.24963\/ijcai.2019\/658"},{"key":"365_CR31","unstructured":"Mikolov,Tomas, Sutskever, Ilya, Chen, Kai, Corrado, Greg\u00a0S, Dean, Jeff: Distributed representations of words and phrases and their compositionality. In C.\u00a0J.\u00a0C. Burges, L.\u00a0Bottou, M.\u00a0Welling, Z.\u00a0Ghahramani, and K.\u00a0Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 3111\u20133119. Curran Associates, Inc., (2013). http:\/\/papers.nips.cc\/paper\/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf"},{"key":"365_CR32","doi-asserted-by":"crossref","unstructured":"Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., Kao, O.: Self-attentive classification-based anomaly detection in unstructured logs. arXiv preprintarXiv:2008.09340, (2020)","DOI":"10.1109\/ICDM50108.2020.00148"},{"key":"365_CR33","doi-asserted-by":"publisher","unstructured":"Oliner, A., Stearley, J.: What supercomputers say: A study of five system logs. In: 37th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN\u201907), pages 575\u2013584, (2007). https:\/\/doi.org\/10.1109\/DSN.2007.103","DOI":"10.1109\/DSN.2007.103"},{"key":"365_CR34","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"365_CR35","first-page":"31","volume":"10","author":"Zuzana Reitermanova","year":"2010","unstructured":"Reitermanova, Zuzana, et al.: Data splitting. WDS 10, 31\u201336 (2010)","journal-title":"WDS"},{"key":"365_CR36","doi-asserted-by":"crossref","unstructured":"Sun, D., Wu, Z., Wang, Y., Lv, Q., Hu, B.: Risk prediction for imbalanced data in cyber security : A siamese network-based deep learning classification framework. In: 2019 International Joint Conference on Neural Networks (IJCNN), pages 1\u20138, (2019)","DOI":"10.1109\/IJCNN.2019.8852030"},{"issue":"Nov","key":"365_CR28","first-page":"2579","volume":"9","author":"Maaten van der Laurens","year":"2008","unstructured":"van der Laurens, Maaten, Geoffrey, Hinton: Visualizing data using t-sne. J. Mach. Learn. Res. 9(Nov), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"365_CR37","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s10515-020-00273-8","volume":"27","author":"Miguel Velez","year":"2020","unstructured":"Velez, Miguel, Jamshidi, Pooyan, Sattler, Florian, Siegmund, Norbert, Apel, Sven, K\u00e4stner, Christian: Configcrusher: towards white-box performance analysis for configurable systems. Autom. Softw. Eng. 27(3), 265\u2013300 (2020). https:\/\/doi.org\/10.1007\/s10515-020-00273-8","journal-title":"Autom. Softw. Eng."},{"key":"365_CR38","doi-asserted-by":"crossref","unstructured":"Wang, W., Yang, J., Xiao, J., Li, S., Zhou, D.: Face recognition based on deep learning. In: International Conference on Human Centered Computing, pages 812\u2013820. Springer, (2014)","DOI":"10.1007\/978-3-319-15554-8_73"},{"key":"365_CR39","unstructured":"Wilkins, B., Watkins, C., Stathis, K.: Anomaly detection in video games. arXiv preprintarXiv:2005.10211, (2020)"},{"key":"365_CR40","unstructured":"Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et\u00a0al.: Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprintarXiv:1609.08144, (2016)"},{"issue":"1","key":"365_CR41","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s10515-014-0162-2","volume":"22","author":"Xin Xia","year":"2015","unstructured":"Xia, Xin, Lo, David, Shihab, Emad, Wang, Xinyu, Zhou, Bo.: Automatic, high accuracy prediction of reopened bugs. Autom. Softw. Eng. 22(1), 75\u2013109 (2015). https:\/\/doi.org\/10.1007\/s10515-014-0162-2. (ISSN 1573-7535)","journal-title":"Autom. Softw. Eng."},{"key":"365_CR42","doi-asserted-by":"crossref","unstructured":"Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M. I.: Detecting large-scale system problems by mining console logs. In: Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, pages 117\u2013132, (2009a)","DOI":"10.1145\/1629575.1629587"},{"key":"365_CR43","doi-asserted-by":"crossref","unstructured":"Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M. I.: Detecting large-scale system problems by mining console logs. In:Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, pages 117\u2013132, (2009b)","DOI":"10.1145\/1629575.1629587"},{"issue":"2","key":"365_CR44","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1145\/2980024.2872407","volume":"44","author":"Yu Xiao","year":"2016","unstructured":"Xiao, Yu., Joshi, Pallavi, Jianwu, Xu., Jin, Guoliang, Zhang, Hui, Jiang, Guofei: Cloudseer: workflow monitoring of cloud infrastructures via interleaved logs. ACM SIGARCH Comput. Archit. News 44(2), 489\u2013502 (2016)","journal-title":"ACM SIGARCH Comput. Archit. News"},{"key":"365_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Xu, Xu, Yong, Lin, Qingwei, Qiao, Bo, Zhang, Hongyu, Dang, Yingnong, Xie, Chunyu, Yang, Xinsheng, Cheng, Qian, Li, Ze, et\u00a0al.: Robust log-based anomaly detection on unstable log data. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 807\u2013817, (2019)","DOI":"10.1145\/3338906.3338931"},{"key":"365_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, L., Qi, J., Wang, D., Feng, M., Lu, H.: Structured siamese network for real-time visual tracking. In: Proceedings of the European conference on computer vision (ECCV), pages 351\u2013366, (2018)","DOI":"10.1007\/978-3-030-01240-3_22"},{"key":"365_CR47","doi-asserted-by":"crossref","unstructured":"Zhu, Jieming, He, Shilin, Liu, Jinyang, He, Pinjia, Xie, Qi, Zheng, Zibin, Lyu, Michael\u00a0R: Tools and benchmarks for automated log parsing. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 121\u2013130. IEEE, (2019)","DOI":"10.1109\/ICSE-SEIP.2019.00021"}],"container-title":["Automated Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-022-00365-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10515-022-00365-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-022-00365-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T11:29:45Z","timestamp":1666956585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10515-022-00365-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["365"],"URL":"https:\/\/doi.org\/10.1007\/s10515-022-00365-7","relation":{},"ISSN":["0928-8910","1573-7535"],"issn-type":[{"value":"0928-8910","type":"print"},{"value":"1573-7535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,13]]},"assertion":[{"value":"31 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"61"}}