{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:15:27Z","timestamp":1742958927237,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811952081"},{"type":"electronic","value":"9789811952098"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-981-19-5209-8_2","type":"book-chapter","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T23:03:17Z","timestamp":1660086197000},"page":"20-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Data Quality Identification Model for\u00a0Power Big Data"],"prefix":"10.1007","author":[{"given":"Haijie","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenqi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"issue":"2","key":"2_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/bdcc5020024","volume":"5","author":"O Azeroual","year":"2021","unstructured":"Azeroual, O., Jha, M.: Without data quality, there is no data migration. Big Data Cogn. Comput. 5(2), 24 (2021)","journal-title":"Big Data Cogn. Comput."},{"key":"2_CR2","unstructured":"Batini, C., Rula, A.: From data quality to big data quality: a data integration scenario. In: Greco, S., Lenzerini, M., Masciari, E., Tagarelli, A. (eds.) Proceedings of the 29th Italian Symposium on Advanced Database Systems, SEBD 2021, Pizzo Calabro (VV), Italy, 5\u20139 September 2021. CEUR Workshop Proceedings, vol. 2994, pp. 36\u201347. CEUR-WS.org (2021)"},{"key":"2_CR3","unstructured":"Bayer, M., Kaufhold, M., Reuter, C.: A survey on data augmentation for text classification. CoRR abs\/2107.03158 (2021)"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.swevo.2011.12.003","volume":"4","author":"BN Biswal","year":"2012","unstructured":"Biswal, B.N., Behera, H.S., Bisoi, R., Dash, P.K.: Classification of power quality data using decision tree and chemotactic differential evolution based fuzzy clustering. Swarm Evol. Comput. 4, 12\u201324 (2012)","journal-title":"Swarm Evol. Comput."},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Blum, A., Mitchell, T.M.: Combining labeled and unlabeled data with co-training. In: Bartlett, P.L., Mansour, Y. (eds.) Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madison, Wisconsin, USA, 24\u201326 July 1998, pp. 92\u2013100. ACM (1998)","DOI":"10.1145\/279943.279962"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Chemnitz, N.\u00d8., Bonnet, P., B\u00fcttrich, S., Shklovski, I., Watts, L.: Unionized data governance in virtual power plants: poster. In: de Meer, H., Meo, M. (eds.) e-Energy 2021: The Twelfth ACM International Conference on Future Energy Systems, Virtual Event, Torino, Italy, 28 June - 2 July 2021, pp. 282\u2013283. ACM (2021)","DOI":"10.1145\/3447555.3466570"},{"key":"2_CR7","unstructured":"Ding, K., Xu, Z., Tong, H., Liu, H.: Data augmentation for deep graph learning: a survey. CoRR abs\/2202.08235 (2022)"},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"146075","DOI":"10.1109\/ACCESS.2020.3015016","volume":"8","author":"RR Exp\u00f3sito","year":"2020","unstructured":"Exp\u00f3sito, R.R., Galego-Torreiro, R., Gonz\u00e1lez-Dom\u00ednguez, J.: SeQual: big data tool to perform quality control and data preprocessing of large NGS datasets. IEEE Access 8, 146075\u2013146084 (2020)","journal-title":"IEEE Access"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Feng, S.Y., Gangal, V., et al.: A survey of data augmentation approaches for NLP. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL\/IJCNLP 2021, Online Event, 1\u20136 August 2021. Findings of ACL, vol. ACL\/IJCNLP 2021, pp. 968\u2013988. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.findings-acl.84"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Fernando, T., Gammulle, H., Denman, S., Sridharan, S., Fookes, C.: Deep learning for medical anomaly detection - a survey. ACM Comput. Surv. 54(7), 141:1\u2013141:37 (2022)","DOI":"10.1145\/3464423"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Hallac, D., Vare, S., Boyd, S.P., Leskovec, J.: Toeplitz inverse covariance-based clustering of multivariate time series data. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13\u201317 August 2017, pp. 215\u2013223. ACM (2017)","DOI":"10.1145\/3097983.3098060"},{"key":"2_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106874","volume":"99","author":"G Lee","year":"2021","unstructured":"Lee, G., Lee, S.J., Lee, C.: A convolutional neural network model for abnormality diagnosis in a nuclear power plant. Appl. Soft Comput. 99, 106874 (2021)","journal-title":"Appl. Soft Comput."},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/LSP.2021.3066068","volume":"28","author":"J Li","year":"2021","unstructured":"Li, J., Wang, G., Chen, M., Ding, Z., Yang, H.: Mixup asymmetric tri-training for heartbeat classification under domain shift. IEEE Signal Process. Lett. 28, 718\u2013722 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"2_CR14","unstructured":"Mohammadi, B., Fathy, M., Sabokrou, M.: Image\/video deep anomaly detection: a survey. CoRR abs\/2103.01739 (2021)"},{"key":"2_CR15","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1007\/978-3-030-85347-1_30","volume-title":"Quality of Information and Communications Technology","author":"O Montero","year":"2021","unstructured":"Montero, O., Crespo, Y., Piatini, M.: Big data quality models: a systematic mapping study. In: Paiva, A.C.R., Cavalli, A.R., Ventura Martins, P., P\u00e9rez-Castillo, R. (eds.) QUATIC 2021. CCIS, vol. 1439, pp. 416\u2013430. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85347-1_30"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Nesen, A., Bhargava, B.K.: Knowledge graphs for semantic-aware anomaly detection in video. In: 3rd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020, Laguna Hills, CA, USA, 9\u201313 December 2020, pp. 65\u201370. IEEE (2020)","DOI":"10.1109\/AIKE48582.2020.00018"},{"key":"2_CR17","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1007\/978-3-030-32388-2_19","volume-title":"Machine Learning and Intelligent Communications","author":"L Qiao","year":"2019","unstructured":"Qiao, L., Zhou, Q., Song, C., Wu, H., Liu, B., Yu, S.: Design of overall framework of self-service big data governance for power grid. In: Zhai, X.B., Chen, B., Zhu, K. (eds.) MLICOM 2019. LNICST, vol. 294, pp. 222\u2013234. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32388-2_19"},{"key":"2_CR18","unstructured":"Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017. Proceedings of Machine Learning Research, vol. 70, pp. 2988\u20132997. PMLR (2017)"},{"issue":"1","key":"2_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00468-0","volume":"8","author":"I Taleb","year":"2021","unstructured":"Taleb, I., Serhani, M.A., Bouhaddioui, C., Dssouli, R.: Big data quality framework: a holistic approach to continuous quality management. J. Big Data 8(1), 1\u201341 (2021). https:\/\/doi.org\/10.1186\/s40537-021-00468-0","journal-title":"J. Big Data"},{"issue":"12","key":"2_CR20","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.3897\/jucs.77046","volume":"27","author":"M Talha","year":"2021","unstructured":"Talha, M., Kalam, A.A.E.: Big data between quality and security: dynamic access control for collaborative platforms. J. Univers. Comput. Sci. 27(12), 1300\u20131324 (2021)","journal-title":"J. Univers. Comput. Sci."},{"issue":"1","key":"2_CR21","doi-asserted-by":"publisher","first-page":"9","DOI":"10.5383\/JUSPN.12.01.002","volume":"12","author":"M Talha","year":"2020","unstructured":"Talha, M., Elmarzouqi, N., Kalam, A.A.E.: Quality and security in big data: challenges as opportunities to build a powerful wrap-up solution. J. Ubiquit. Syst. Perv. Netw. 12(1), 9\u201315 (2020)","journal-title":"J. Ubiquit. Syst. Perv. Netw."},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Wen, Q., et al.: Time series data augmentation for deep learning: a survey. In: Zhou, Z. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event \/ Montreal, Canada, 19\u201327 August 2021, pp. 4653\u20134660. ijcai.org (2021)","DOI":"10.24963\/ijcai.2021\/631"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Gao, M., Xia, X., Zhang, X., Hung, N.Q.V.: Socially-aware self-supervised tri-training for recommendation. In: Zhu, F., Ooi, B.C., Miao, C. (eds.) KDD 2021: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, 14\u201318 August 2021, pp. 2084\u20132092. ACM (2021)","DOI":"10.1145\/3447548.3467340"},{"key":"2_CR24","unstructured":"Yuan, S., Wu, X.: Trustworthy anomaly detection: a survey. CoRR abs\/2202.07787 (2022)"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Zakaria, J., Mueen, A., Keogh, E.J.: Clustering time series using unsupervised-shapelets. In: Zaki, M.J., Siebes, A., Yu, J.X., Goethals, B., Webb, G.I., Wu, X. (eds.) 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, 10\u201313 December 2012, pp. 785\u2013794. IEEE Computer Society (2012)","DOI":"10.1109\/ICDM.2012.26"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, J.E., Wu, D., Boulet, B.: Time series anomaly detection for smart grids: a survey. CoRR abs\/2107.08835 (2021)","DOI":"10.1109\/EPEC52095.2021.9621752"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, B., Shi, Y., Zhang, K., Yan, Z.: Health insurance anomaly detection based on dynamic heterogeneous information network. In: Yoo, I., Bi, J., Hu, X. (eds.) 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, CA, USA, 18\u201321 November 2019, pp. 1118\u20131122. IEEE (2019)","DOI":"10.1109\/BIBM47256.2019.8983130"},{"key":"2_CR28","unstructured":"Zhao, T., Liu, G., G\u00fcnnemann, S., Jiang, M.: Graph data augmentation for graph machine learning: a survey. CoRR abs\/2202.08871 (2022)"},{"issue":"11","key":"2_CR29","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1109\/TKDE.2005.186","volume":"17","author":"Z Zhou","year":"2005","unstructured":"Zhou, Z., Li, M.: Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans. Knowl. Data Eng. 17(11), 1529\u20131541 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-3-030-00006-6_56","volume-title":"Cloud Computing and Security","author":"H Zhu","year":"2018","unstructured":"Zhu, H., Liu, J., Wan, M.: Label noise detection based on tri-training. In: Sun, X., Pan, Z., Bertino, E. (eds.) ICCCS 2018. LNCS, vol. 11063, pp. 613\u2013622. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00006-6_56"}],"container-title":["Communications in Computer and Information Science","Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-5209-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T02:24:36Z","timestamp":1727749476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-5209-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811952081","9789811952098"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-5209-8_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPCSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Pioneering Computer Scientists, Engineers and Educators","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpcsee2022","order":10,"name":"conference_id","label":"Conference ID","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"261","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":"65","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":"26","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":"25% - 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":"5","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)"}}]}}