{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T14:07:48Z","timestamp":1749046068402,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811631498"},{"type":"electronic","value":"9789811631504"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-3150-4_43","type":"book-chapter","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T12:03:10Z","timestamp":1624276990000},"page":"522-536","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hard Disk Failure Prediction via Transfer Learning"],"prefix":"10.1007","author":[{"given":"Rui","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghai","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanfeng","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaofeng","family":"Tu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asad Masood","family":"Khattak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"key":"43_CR1","doi-asserted-by":"crossref","unstructured":"Vishwanath, K.V., Nagappan, N.: Characterizing cloud computing hardware reliability. In: Proceedings of the 1st ACM symposium on Cloud computing, pp. 193\u2013204 (2010)","DOI":"10.1145\/1807128.1807161"},{"key":"43_CR2","first-page":"74","volume":"117","author":"B Allen","year":"2004","unstructured":"Allen, B.: Monitoring hard disks with smart. Linux J. 117, 74\u201377 (2004)","journal-title":"Linux J."},{"key":"43_CR3","doi-asserted-by":"crossref","unstructured":"Eckart, B., Chen, X., He X., et al.: Failure prediction models for proactive fault tolerance within storage systems. In: 2008 IEEE International Symposium on Modeling, Analysis and Simulation of Computers and Telecommunication Systems, pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/MASCOT.2008.4770560"},{"issue":"May","key":"43_CR4","first-page":"783","volume":"6","author":"JF Murray","year":"2005","unstructured":"Murray, J.F., Hughes, G.F., Kreutz-Delgado, K.: Machine learning methods for predicting failures in hard drives: a multiple-instance application. J. Mach. Learn. Res. 6(May), 783\u2013816 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"43_CR5","doi-asserted-by":"crossref","unstructured":"Xiao, J., Xiong, Z., Wu, S., et al.: Disk failure prediction in data centers via online learning. In: Proceedings of the 47th International Conference on Parallel Processing, pp. 1\u201310 (2018)","DOI":"10.1145\/3225058.3225106"},{"key":"43_CR6","doi-asserted-by":"crossref","unstructured":"Li, J., Ji, X., Jia, Y., et al.: Hard drive failure prediction using classification and regression trees. In: 2014 44th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks, pp. 383\u2013394. IEEE (2014)","DOI":"10.1109\/DSN.2014.44"},{"key":"43_CR7","unstructured":"Murray, J.F., Hughes, G.F., Kreutz-Delgado, K.: Hard drive failure prediction using non-parametric statistical methods. In: Proceedings of ICANN\/ICONIP (2003)"},{"key":"43_CR8","doi-asserted-by":"crossref","unstructured":"Yang, W., Hu, D., Liu, Y., et al.: Hard drive failure prediction using big data. In: 2015 IEEE 34th Symposium on Reliable Distributed Systems Workshop (SRDSW), pp. 13\u201318. IEEE (2015)","DOI":"10.1109\/SRDSW.2015.15"},{"key":"43_CR9","doi-asserted-by":"publisher","unstructured":"Zhao, Y., Liu, X., Gan, S., et al.: Predicting disk failures with HMM-and HSMM-based approaches. In: Industrial Conference on Data Mining, pp. 390\u2013404. Springer, Berlin, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-14400-4_30","DOI":"10.1007\/978-3-642-14400-4_30"},{"key":"43_CR10","doi-asserted-by":"crossref","unstructured":"Botezatu, M.M., Giurgiu, I., Bogojeska, J., et al.: Predicting disk replacement towards reliable data centers. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 39\u201348 (2016)","DOI":"10.1145\/2939672.2939699"},{"key":"43_CR11","doi-asserted-by":"crossref","unstructured":"Zhu, B., Wang, G., Liu, X., et al.: Proactive drive failure prediction for large scale storage systems. In: 2013 IEEE 29th symposium on mass storage systems and technologies (MSST), pp. 1\u20135. IEEE (2013)","DOI":"10.1109\/MSST.2013.6558427"},{"key":"43_CR12","unstructured":"Xu, Y., Sui, K., Yao, R., et al.: Improving service availability of cloud systems by predicting disk error. In: 2018 {USENIX} Annual Technical Conference ({USENIX}{ATC} 18), pp. 481\u2013494 (2018)"},{"key":"43_CR13","doi-asserted-by":"crossref","unstructured":"Pereira, F.L.F., dos Santos Lima, F D., de Moura Leite, L G., et al.: Transfer learning for Bayesian networks with application on hard disk drives failure prediction. In: 2017 Brazilian Conference on Intelligent Systems (BRACIS), pp. 228\u2013233. IEEE (2017)","DOI":"10.1109\/BRACIS.2017.64"},{"issue":"3","key":"43_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1416944.1416946","volume":"4","author":"W Jiang","year":"2008","unstructured":"Jiang, W., Hu, C., Zhou, Y., et al.: Are disks the dominant contributor for storage failures? a comprehensive study of storage subsystem failure characteristics. ACM Trans. Storage (TOS). 4(3), 1\u201325 (2008)","journal-title":"ACM Trans. Storage (TOS)."},{"key":"43_CR15","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International conference on machine learning, pp. 1180\u20131189 (2015)"},{"issue":"3","key":"43_CR16","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1109\/TR.2002.802886","volume":"51","author":"GF Hughes","year":"2002","unstructured":"Hughes, G.F., Murray, J.F., Kreutz-Delgado, K., et al.: Improved disk-drive failure warnings. IEEE Trans. Reliab. 51(3), 350\u2013357 (2002)","journal-title":"IEEE Trans. Reliab."},{"key":"43_CR17","first-page":"202","volume":"1","author":"G Hamerly","year":"2001","unstructured":"Hamerly, G., Elkan, C.: Bayesian approaches to failure prediction for disk drives. ICML 1, 202\u2013209 (2001)","journal-title":"ICML"},{"issue":"1","key":"43_CR18","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1109\/TII.2013.2264060","volume":"10","author":"Y Wang","year":"2013","unstructured":"Wang, Y., Ma, E.W.M., Chow, T.W.S., et al.: A two-step parametric method for failure prediction in hard disk drives. IEEE Trans. Ind. Inf. 10(1), 419\u2013430 (2013)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"11","key":"43_CR19","doi-asserted-by":"publisher","first-page":"3502","DOI":"10.1109\/TC.2016.2538237","volume":"65","author":"C Xu","year":"2016","unstructured":"Xu, C., Wang, G., Liu, X., et al.: Health status assessment and failure prediction for hard drives with recurrent neural networks. IEEE Trans. Comput. 65(11), 3502\u20133508 (2016)","journal-title":"IEEE Trans. Comput."},{"key":"43_CR20","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neunet.2019.07.010","volume":"119","author":"Y Zhu","year":"2019","unstructured":"Zhu, Y., Zhuang, F., Wang, J., et al.: Multi-representation adaptation network for cross-domain image classification. Neural Netw. 119, 214\u2013221 (2019)","journal-title":"Neural Netw."},{"key":"43_CR21","unstructured":"Prettenhofer, P., Stein, B.: Cross-language text classification using structural correspondence learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics, pp. 1118\u20131127 (2010)"},{"key":"43_CR22","doi-asserted-by":"crossref","unstructured":"Wang, J., Zheng, V.W., Chen, Y., et al.: Deep transfer learning for cross-domain activity recognition. In: proceedings of the 3rd International Conference on Crowd Science and Engineering, pp. 1\u20138 (2018)","DOI":"10.1145\/3265689.3265705"},{"issue":"5","key":"43_CR23","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., Goldbaum, M., Cai, W., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122\u20131131 (2018)","journal-title":"Cell"},{"key":"43_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhou, K., Huang, P., et al.: Transfer learning based failure prediction for minority disks in large data centers of heterogeneous disk systems. In: Proceedings of the 48th International Conference on Parallel Processing, pp.1\u201310 (2019)","DOI":"10.1145\/3337821.3337881"},{"key":"43_CR25","doi-asserted-by":"crossref","unstructured":"Jiang, T., Zeng, J., Zhou, K., et al.: Lifelong disk failure prediction via GAN-Based anomaly detection. In: 2019 IEEE 37th International Conference on Computer Design (ICCD), pp. 199\u2013207. IEEE (2019)","DOI":"10.1109\/ICCD46524.2019.00033"},{"key":"43_CR26","unstructured":"Zhang, X., Kim, J., Lin, Q., et al.: Cross-dataset time series anomaly detection for cloud systems. In: 2019 {USENIX} Annual Technical Conference ({USENIX}{ATC} 19), pp. 1063\u20131076 (2019)"},{"issue":"2","key":"43_CR27","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/S0378-3758(00)00115-4","volume":"90","author":"H Shimodaira","year":"2000","unstructured":"Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Stat. Plan. Infer. 90(2), 227\u2013244 (2000)","journal-title":"J. Stat. Plan. Infer."},{"key":"43_CR28","doi-asserted-by":"crossref","unstructured":"Yang, Y., Loog, M.: Active learning using uncertainty information. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2646\u20132651. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7900034"},{"key":"43_CR29","unstructured":"Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)"},{"issue":"9","key":"43_CR30","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263\u20131284 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"43_CR31","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"43_CR32","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., et al.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/335191.335388"},{"key":"43_CR33","unstructured":"dos Santos Lima, F.D., Amaral, G.M.R., de Moura Leite, L.G., et al.: Predicting failures in hard drives with lstm networks. In: 2017 Brazilian Conference on Intelligent Systems (BRACIS), pp. 222\u2013227. IEEE (2017)"}],"container-title":["Communications in Computer and Information Science","Big Data and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-3150-4_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T12:22:21Z","timestamp":1624278141000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-3150-4_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811631498","9789811631504"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-3150-4_43","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICBDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"20 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbds2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icbds.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"153","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":"44","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":"8","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":"29% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}