{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:53:14Z","timestamp":1742914394131,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819712793"},{"type":"electronic","value":"9789819712809"}],"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\/978-981-97-1280-9_5","type":"book-chapter","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T06:01:41Z","timestamp":1712037701000},"page":"59-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["O&M Portrait Tag Generation and Management of Grid Business Application System Under Microservice Architecture"],"prefix":"10.1007","author":[{"given":"Dequan","family":"Gao","sequence":"first","affiliation":[]},{"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Bao","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Shao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Liang, H., Ma, J.: Data-driven resource planning for virtual power plant integrating demand response customer selection and storage. IEEE Trans. Ind. Inf. 18, 1833\u201344 (2021)","DOI":"10.1109\/TII.2021.3068402"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Rahdari, F., Movahhedinia, N., Khayyambashi, M., Valaee, S.: QoE-aware power control and user grouping in cognitive radio OFDM-NOMA systems. Comput. Networks 189, 107906 (2021)","DOI":"10.1016\/j.comnet.2021.107906"},{"key":"5_CR3","unstructured":"Cooper.: The Inmates are running the asylum. In: Publishing House of Electronics Industry (2006)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Gu, H., Wang, J., Wang, Z., et al.: Modeling of user portrait through social media. In: IEEE International Conference on Multimedia, pp. 1\u20136 (2018)","DOI":"10.1109\/ICME.2018.8486595"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Huang, K.H., Deng, Y.S., Chuang, M.C.: Static and dynamic user portraits. Adv. Hum. Comput. Interact. 123725, 1\u20136 (2012)","DOI":"10.1155\/2012\/123725"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Xiong, R., Donath, J.: PeopleGarden: creating data portraits for users. In: ACM Symposium on User Interface Software and Technology (1999)","DOI":"10.1145\/320719.322581"},{"key":"5_CR7","unstructured":"Rosenthal, S., McKeown, K.: Age prediction in blogs: a study of style, content, and online behavior in pre- and post-social media generations. In: Annual Meeting of the Association for Computational Linguistics (2011)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Mueller, J., Stumme, G.: Gender inference using statistical name characteristics in Twitter. In: Proceedings of the 3rd Multidisciplinary International Social Networks Conference on SocialInformatics, Data Science (2016)","DOI":"10.1145\/2955129.2955182"},{"issue":"8","key":"5_CR9","first-page":"332","volume":"37","author":"N Guo","year":"2020","unstructured":"Guo, N., Wei, R.K., Shen, Y.P.: Abnormal feature extraction method in large data environment based on user portrait. Comput. Simul. 37(8), 332\u2013336 (2020)","journal-title":"Comput. Simul."},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Chicaiza, J., D\u00edaz, P.V.: A comprehensive survey of knowledge graph-based recommender systems: technologies, development, and contributions. Information 12, 232 (2021)","DOI":"10.3390\/info12060232"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, J., Huang, W., Ji, D., et al.: Globally normalized neural model for joint entity and event extraction. Inf. Process. Manag. 58, 102636 (2021)","DOI":"10.1016\/j.ipm.2021.102636"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Cern\u00fd, T., Donahoo, M., Trnka, M.: Contextual understanding of microservice architecture: current and future directions. ACM Sigapp Appl. Comput. Rev. 17, 29\u201345 (2018)","DOI":"10.1145\/3183628.3183631"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Cern\u00fd, T., Abdelfattah, A.S., Bushong, V., et al.: Microservice architecture reconstruction and visualization techniques: a review. In: IEEE International Conference on Service-Oriented System Engineering, pp. 39\u201348 (2022)","DOI":"10.1109\/SOSE55356.2022.00011"},{"key":"5_CR14","unstructured":"Tetiana, Y., Bagge, A.H.: Overcoming security challenges in microservice architectures. In: 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE), IEEE (2018)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Gortney, M.E., Harris, P.E., Cern\u00fd, T., et al.: Visualizing microservice architecture in the dynamic perspective: a systematic mapping study. IEEE Access 10, 119999\u201320012 (2022)","DOI":"10.1109\/ACCESS.2022.3221130"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Blinowski, G., Ojdowska, A., Przyby\u0142ek, A.: Monolithic vs. microservice architecture: a performance and scalability evaluation. IEEE Access 10, 20357\u201320374 (2022)","DOI":"10.1109\/ACCESS.2022.3152803"},{"key":"5_CR17","unstructured":"Bandyopadhyay, S., Datta, A., Pal, A.: Automated label generation for time series classification with representation learning: reduction of label cost for training. arXiv preprint arXiv:2107.05458 (2021)"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Tang, R., Zeng, F., Chen, Z., et al.: The comparison of predicting storm-time ionospheric TEC by three methods: aRIMA, LSTM, and Seq2Seq. Atmosphere (2020)","DOI":"10.3390\/atmos11040316"},{"issue":"6","key":"5_CR19","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/BF02834632","volume":"4","author":"GJ McLachlan","year":"1999","unstructured":"McLachlan, G.J.: Mahalanobis distance. Resonance 4(6), 20\u201326 (1999)","journal-title":"Resonance"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Mattiev, J., Kav\u0161ek, B.: CMAC: clustering class association rules to form a compact and meaningful associative classifier. In: International Conference on Machine Learning, Optimization, and Data Science (2020)","DOI":"10.1007\/978-3-030-64583-0_34"}],"container-title":["Communications in Computer and Information Science","Data Science and Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-1280-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T06:10:25Z","timestamp":1712038225000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-1280-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819712793","9789819712809"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-1280-9_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IAIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Artificial Intelligence Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iaic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iaicconf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}