{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T03:10:06Z","timestamp":1743995406546,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031877681","type":"print"},{"value":"9783031877698","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-87769-8_33","type":"book-chapter","created":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T02:36:42Z","timestamp":1743993402000},"page":"377-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["On the\u00a0Challenges of\u00a0Implementing MLOps for\u00a0Stream Learning Algorithms"],"prefix":"10.1007","author":[{"given":"Miguel G.","family":"Rodrigues","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo K.","family":"Viegas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabr\u00edcio","family":"Enembreck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Altair O.","family":"Santin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juliano S.","family":"Langaro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adilson G.","family":"Filho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"issue":"10","key":"33_CR1","doi-asserted-by":"publisher","first-page":"9523","DOI":"10.1016\/j.jksuci.2021.11.006","volume":"34","author":"S Agrahari","year":"2022","unstructured":"Agrahari, S., Singh, A.K.: Concept drift detection in data stream mining: a literature review. J. King Saud Univ. Comput. Inf. Sci0 34(10), 9523\u20139540 (2022)","journal-title":"J. King Saud Univ. Comput. Inf. Sci0"},{"key":"33_CR2","unstructured":"Apache Software Foundation: Apache hadoop (2006). https:\/\/hadoop.apache.org\/"},{"key":"33_CR3","unstructured":"Apache Software Foundation: Hadoop distributed file system (hdfs) (2006). https:\/\/hadoop.apache.org\/docs\/stable\/hadoop-project-dist\/hadoop-hdfs\/HdfsUserGuide.html"},{"key":"33_CR4","unstructured":"Apache Software Foundation: Apache kafka (2011). https:\/\/kafka.apache.org\/"},{"key":"33_CR5","unstructured":"Apache Software Foundation: Apache spark (2013). https:\/\/spark.apache.org\/"},{"key":"33_CR6","unstructured":"Apache Software Foundation: Spark streaming (2013). https:\/\/spark.apache.org\/docs\/latest\/streaming-programming-guide.html"},{"key":"33_CR7","unstructured":"Apache Software Foundation: Apache flink: Stateful computations over data streams (2014). https:\/\/flink.apache.org\/"},{"key":"33_CR8","unstructured":"Apache Software Foundation: Mllib: Machine learning library (2014). https:\/\/spark.apache.org\/mllib\/"},{"issue":"5","key":"33_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3453444","volume":"54","author":"R Ashmore","year":"2021","unstructured":"Ashmore, R., Calinescu, R., Paterson, C.: Assuring the machine learning lifecycle: desiderata, methods, and challenges. ACM Comput. Surv. 54(5), 1\u201339 (2021)","journal-title":"ACM Comput. Surv."},{"key":"33_CR10","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.jss.2016.07.005","volume":"127","author":"JP Barddal","year":"2017","unstructured":"Barddal, J.P., Gomes, H.M., Enembreck, F., Pfahringer, B.: A survey on feature drift adaptation: definition, benchmark, challenges and future directions. J. Syst. Softw. 127, 278\u2013294 (2017)","journal-title":"J. Syst. Softw."},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Barry, M., et al.: Streammlops: operationalizing online learning for big data streaming & real-time applications. In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp. 3508\u20133521. IEEE (2023)","DOI":"10.1109\/ICDE55515.2023.00272"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Baylor, D., et al.: TFX: a tensorflow-based production-scale machine learning platform. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2017, ACM (2017)","DOI":"10.1145\/3097983.3098021"},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Bifet, A., Maniu, S., Qian, J., Tian, G., He, C., Fan, W.: Streamdm: advanced data mining in spark streaming. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE (2015)","DOI":"10.1109\/ICDMW.2015.140"},{"key":"33_CR14","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.inffus.2017.09.005","volume":"41","author":"F Carcillo","year":"2018","unstructured":"Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.A., Caelen, O., Mazzer, Y., Bontempi, G.: SCARFF\u202f: a scalable framework for streaming credit card fraud detection with spark. Inf. Fusion 41, 182\u2013194 (2018)","journal-title":"Inf. Fusion"},{"key":"33_CR15","unstructured":"Elastic: Elasticsearch (2010). https:\/\/www.elastic.co\/elasticsearch\/"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Espindola, A., Viegas, E.K., Traleski, A., Pellenz, M.E., Santin, A.O.: A deep autoencoder and RNN model for indoor localization with variable propagation loss. In: 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE (2021)","DOI":"10.1109\/WiMob52687.2021.9606346"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Garcia-Rodriguez, S., Alshaer, M., Gouy-Pailler, C.: Streamer: a powerful framework for continuous learning in data streams. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. CIKM 2020, ACM (2020)","DOI":"10.1145\/3340531.3417427"},{"issue":"18","key":"33_CR18","doi-asserted-by":"publisher","first-page":"8144","DOI":"10.1016\/j.eswa.2014.07.019","volume":"41","author":"PM Gon\u00e7alves","year":"2014","unstructured":"Gon\u00e7alves, P.M., de Carvalho Santos, S.G., Barros, R.S., Vieira, D.C.: A comparative study on concept drift detectors. Expert Syst. Appl. 41(18), 8144\u20138156 (2014)","journal-title":"Expert Syst. Appl."},{"key":"33_CR19","unstructured":"Google Brain Team: Tensorflow: An open-source machine learning framework (2015). https:\/\/www.tensorflow.org\/"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Horchulhack, P., Viegas, E.K., Santin, A.O.: Detection of service provider hardware over-commitment in container orchestration environments. In: GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pp. 6354\u20136359. IEEE (2022)","DOI":"10.1109\/GLOBECOM48099.2022.10001375"},{"key":"33_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2024.103839","volume":"224","author":"P Horchulhack","year":"2024","unstructured":"Horchulhack, P., Viegas, E.K., Santin, A.O., Ramos, F.V., Tedeschi, P.: Detection of quality of service degradation on multi-tenant containerized services. J. Netw. Comput. Appl. 224, 103839 (2024). https:\/\/doi.org\/10.1016\/j.jnca.2024.103839","journal-title":"J. Netw. Comput. Appl."},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Jamil, W., Duong, N.C., Wang, W., Mansouri, C., Mohamad, S., Bouchachia, A.: Scalable online learning for flink: Solma library. In: Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings. ECSA 2018, vol.\u00a058, pp. 1\u20134. ACM (2018)","DOI":"10.1145\/3241403.3241438"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"John, M.M., Olsson, H.H., Bosch, J., Gillblad, D.: Exploring trade-offs in MLOps adoption. In: 2023 30th Asia-Pacific Software Engineering Conference (APSEC), pp. 369\u2013375. IEEE (2023)","DOI":"10.1109\/APSEC60848.2023.00047"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Komisarek, M., Chora\u015b, M., Kozik, R., Pawlicki, M.: Real-time stream processing tool for detecting suspicious network patterns using machine learning. In: Proceedings of the 15th International Conference on Availability, Reliability and Security. ARES 2020, ACM (2020)","DOI":"10.1145\/3407023.3409189"},{"key":"33_CR25","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.future.2016.07.018","volume":"68","author":"J Kranjc","year":"2017","unstructured":"Kranjc, J., Ora\u010d, R., Podpe\u010dan, V., Lavra\u010d, N., Robnik-\u0160ikonja, M.: Clowdflows: online workflows for distributed big data mining. Futur. Gener. Comput. Syst. 68, 38\u201358 (2017)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"33_CR26","doi-asserted-by":"publisher","first-page":"31866","DOI":"10.1109\/ACCESS.2023.3262138","volume":"11","author":"D Kreuzberger","year":"2023","unstructured":"Kreuzberger, D., K\u00fchl, N., Hirschl, S.: Machine learning operations (MLOps): overview, definition, and architecture. IEEE Access 11, 31866\u201331879 (2023)","journal-title":"IEEE Access"},{"key":"33_CR27","unstructured":"Kubernetes Authors: Kubernetes: Open-source container orchestration platform (2014). https:\/\/kubernetes.io\/"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Markov, I.L., et al.: Looper: an end-to-end ml platform for product decisions. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD 2022, vol.\u00a028, pp. 3513\u20133523. ACM (2022)","DOI":"10.1145\/3534678.3539059"},{"key":"33_CR29","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.future.2021.07.037","volume":"126","author":"C Mart\u00edn","year":"2022","unstructured":"Mart\u00edn, C., Langendoerfer, P., Zarrin, P.S., D\u00edaz, M., Rubio, B.: Kafka-ml: connecting the data stream with ML\/AI frameworks. Futur. Gener. Comput. Syst. 126, 15\u201333 (2022)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"33_CR30","unstructured":"MLflow Project: An open source platform for the machine learning lifecycle (2018). https:\/\/mlflow.org\/"},{"key":"33_CR31","unstructured":"Montiel, J., et al.: River: machine learning for streaming data in python (2021). https:\/\/riverml.xyz\/"},{"key":"33_CR32","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.comcom.2023.11.002","volume":"213","author":"E Neto","year":"2024","unstructured":"Neto, E., Dadkhah, S., Sadeghi, S., Molyneaux, H., Ghorbani, A.A.: A review of machine learning (ml)-based IoT security in healthcare: a dataset perspective. Comput. Commun. 213, 61\u201377 (2024)","journal-title":"Comput. Commun."},{"issue":"6","key":"33_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533378","volume":"55","author":"A Paleyes","year":"2022","unstructured":"Paleyes, A., Urma, R.G., Lawrence, N.D.: Challenges in deploying machine learning: a survey of case studies. ACM Comput. Surv. 55(6), 1\u201329 (2022)","journal-title":"ACM Comput. Surv."},{"key":"33_CR34","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"12","key":"33_CR35","doi-asserted-by":"publisher","first-page":"3917","DOI":"10.1007\/s00167-022-07155-4","volume":"30","author":"JA Pruneski","year":"2022","unstructured":"Pruneski, J.A., et al.: The development and deployment of machine learning models. Knee Surg. Sports Traumatol. Arthrosc. 30(12), 3917\u20133923 (2022)","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"33_CR36","unstructured":"PyTorch Foundation: Pytorch: An open-source machine learning library (2016). https:\/\/pytorch.org\/"},{"key":"33_CR37","doi-asserted-by":"crossref","unstructured":"Ramanath, R., et al.: Lambda learner: fast incremental learning on data streams. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. KDD 2021, ACM (2021)","DOI":"10.1145\/3447548.3467172"},{"key":"33_CR38","doi-asserted-by":"crossref","unstructured":"Ribeiro, R., Santin, A., Abreu, V., Marynowski, J., Viegas, E.: Providing security and privacy in smart house through mobile cloud computing. In: 2016 8th IEEE Latin-American Conference on Communications (LATINCOM), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/LATINCOM.2016.7811599"},{"key":"33_CR39","doi-asserted-by":"crossref","unstructured":"dos Santos, R.R., Viegas, E.K., Santin, A.O., Tedeschi, P.: Federated learning for reliable model updates in network-based intrusion detection. Comput. Secur. 133 (2023)","DOI":"10.1016\/j.cose.2023.103413"},{"key":"33_CR40","unstructured":"The Apache Software Foundation: Apache airflow (2014). https:\/\/airflow.apache.org\/"},{"key":"33_CR41","unstructured":"The Kubeflow Authors: Kubeflow (2018). https:\/\/www.kubeflow.org\/"},{"key":"33_CR42","doi-asserted-by":"crossref","unstructured":"Viegas, E., Santin, A., Neves, N., Bessani, A., Abreu, V.: A resilient stream learning intrusion detection mechanism for real-time analysis of network traffic. In: GLOBECOM 2017 - 2017 IEEE Global Communications Conference, pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/GLOCOM.2017.8254495"},{"key":"33_CR43","doi-asserted-by":"crossref","unstructured":"Xu, D., Wu, D., Xu, X., Zhu, L., Bass, L.: Making real time data analytics available as a service. In: Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures, pp. 73\u201382. CompArch 2015, ACM (2015)","DOI":"10.1145\/2737182.2737186"},{"key":"33_CR44","first-page":"39","volume":"41","author":"MA Zaharia","year":"2018","unstructured":"Zaharia, M.A., et al.: Accelerating the machine learning lifecycle with MLflow. IEEE Data Eng. Bull. 41, 39\u201345 (2018)","journal-title":"IEEE Data Eng. Bull."}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Advanced Information Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-87769-8_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T02:37:23Z","timestamp":1743993443000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-87769-8_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031877681","9783031877698"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-87769-8_33","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"value":"2367-4512","type":"print"},{"value":"2367-4520","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"8 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AINA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Information Networking and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Barcelona","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"39","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aina0","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/aina\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}