{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:36:52Z","timestamp":1770525412214,"version":"3.49.0"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159922","type":"print"},{"value":"9783032159939","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-15993-9_30","type":"book-chapter","created":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T05:52:14Z","timestamp":1770443534000},"page":"441-455","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parallelization Strategies for\u00a0the\u00a0Feature Space Partition Algorithm Applied to\u00a0Fault Detection and\u00a0Stability Analysis in\u00a0Smart Grids"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3432-9996","authenticated-orcid":false,"given":"Saulo Andrade","family":"Almeida","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2187-5442","authenticated-orcid":false,"given":"Silvana","family":"Rossetto","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7595-8227","authenticated-orcid":false,"given":"Carolina Gil","family":"Marcelino","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,8]]},"reference":[{"key":"30_CR1","unstructured":"Almeida, S.A., Carmo, B.A.: FSP-PARALLEL (2024). https:\/\/doi.org\/10.5281\/zenodo.15048154 Acessado 25-Junho-2025"},{"key":"30_CR2","doi-asserted-by":"publisher","unstructured":"Arzamasov, V.: Electrical Grid Stability Simulated Data. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5PG66","DOI":"10.24432\/C5PG66"},{"key":"30_CR3","unstructured":"Ben-Haim, Y., Tom-Tov, E.: A streaming parallel decision tree algorithm. J. Mach. Learn. Res. 11(2) (2010)"},{"issue":"2","key":"30_CR4","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1002\/sam.10006","volume":"1","author":"K Bhaduri","year":"2008","unstructured":"Bhaduri, K., Wolff, R., Giannella, C., Kargupta, H.: Distributed decision-tree induction in peer-to-peer systems. Statist. Analy. Data Mining: ASA Data Sci. J. 1(2), 85\u2013103 (2008)","journal-title":"Statist. Analy. Data Mining: ASA Data Sci. J."},{"key":"30_CR5","unstructured":"Billingsley, P.: Probability and measure. John Wiley & Sons (1995)"},{"key":"30_CR6","unstructured":"Carmo, B.A.d.: Fsp learn (2025). https:\/\/doi.org\/10.5281\/zenodo.15379896 Acessado 15-Fevereiro-2025"},{"key":"30_CR7","doi-asserted-by":"publisher","unstructured":"Daniel\u00a0Dias, S.P., Bscaro, H.: Libras Movement. UCI Machine Learning Repository (2009). https:\/\/doi.org\/10.24432\/C5GC82","DOI":"10.24432\/C5GC82"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Desai, A., Chaudhary, S.: Distributed decision tree. In: Proceedings of the 9th annual ACM India Conference, pp. 43\u201350 (2016)","DOI":"10.1145\/2998476.2998478"},{"key":"30_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/978-3-540-88192-6_15","volume-title":"Advanced Data Mining and Applications","author":"T-N Do","year":"2008","unstructured":"Do, T.-N., Nguyen, V.-H., Poulet, F.: Speed Up SVM algorithm for massive classification tasks. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds.) ADMA 2008. LNCS (LNAI), vol. 5139, pp. 147\u2013157. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-88192-6_15"},{"key":"30_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-3-662-54173-9_4","volume-title":"Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXI","author":"T-N Do","year":"2017","unstructured":"Do, T.-N., Poulet, F.: Parallel learning of Local SVM algorithms for classifying large datasets. In: Hameurlain, A., K\u00fcng, J., Wagner, R., Dang, T.K., Thoai, N. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXI. LNCS, vol. 10140, pp. 67\u201393. Springer, Heidelberg (2017). https:\/\/doi.org\/10.1007\/978-3-662-54173-9_4"},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Dobriban, E., Sheng, Y.: Distributed linear regression by averaging. Annals Statist. 49(2), 918 \u2013 943 (2021). https:\/\/doi.org\/10.1214\/20-AOS1984","DOI":"10.1214\/20-AOS1984"},{"key":"30_CR12","doi-asserted-by":"publisher","unstructured":"Evans, B.: Cylinder Bands. UCI Machine Learning Repository (1995). https:\/\/doi.org\/10.24432\/C50C7B","DOI":"10.24432\/C50C7B"},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Fisher, R.A.: Iris. UCI Machine Learning Repository (1988). https:\/\/doi.org\/10.24432\/C56C76","DOI":"10.24432\/C56C76"},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"German, B.: Glass Identification. UCI Machine Learning Repository (1987). https:\/\/doi.org\/10.24432\/C5WW2P","DOI":"10.24432\/C5WW2P"},{"key":"30_CR15","doi-asserted-by":"publisher","unstructured":"Jin, R., Agrawal, G.: Communication and Memory Efficient Parallel Decision Tree Construction, pp. 119\u2013129. Society for Insdustrial and Applied Mathematics (2003). https:\/\/doi.org\/10.1137\/1.9781611972733.11, https:\/\/epubs.siam.org\/doi\/abs\/10.1137\/1.9781611972733.11","DOI":"10.1137\/1.9781611972733.11"},{"key":"30_CR16","unstructured":"Kaggle datasets: Kaggle datasets (2011). https:\/\/www.kaggle.com\/datasets Acessado 26-Novembro-2024"},{"key":"30_CR17","unstructured":"Keras: Keras (2024). https:\/\/keras.io Acessado 8-Outubro-2024"},{"key":"30_CR18","doi-asserted-by":"publisher","unstructured":"Kulkarni, J.B., Sawant, A.A., Inamdar, V.S.: Database processing by linear regression on gpu using cuda. In: 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies, pp. 20\u201323 (July 2011). https:\/\/doi.org\/10.1109\/ICSCCN.2011.6024507","DOI":"10.1109\/ICSCCN.2011.6024507"},{"key":"30_CR19","doi-asserted-by":"publisher","unstructured":"Marcelino, C.G., Pedreira, C.E.: Feature space partition: a local\u2013global approach for classification. Neural Comput. Appl. 34(24), 21877\u201321890 (2022). https:\/\/doi.org\/10.1007\/s00521-022-07647-x","DOI":"10.1007\/s00521-022-07647-x"},{"key":"30_CR20","unstructured":"Mohri, M.: Foundations of machine learning (2018)"},{"key":"30_CR21","unstructured":"OpenMP Architecture Review Board: OpenMP application program interface version 3.0 (May 2008). http:\/\/www.openmp.org\/mp-documents\/spec30.pdf"},{"key":"30_CR22","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."},{"key":"30_CR23","unstructured":"Prakash, E.S.: Electrical Fault Detection and Classification. Kaggle (2021). https:\/\/www.kaggle.com\/datasets\/esathyaprakash\/electrical-fault-detection-and-classification"},{"key":"30_CR24","unstructured":"Principe, J., Xu, D., Fisher, J., Haykin, S.: Information theoretic learning. unsupervised adaptive filtering. Unsupervised Adapt Filter 1 (2000)"},{"key":"30_CR25","unstructured":"pyinstrument: pyinstrument (2024). https:\/\/pyinstrument.readthedocs.io Acessado 12-Outubro-2024"},{"key":"30_CR26","unstructured":"PyTorch: Pytorch (2024). https:\/\/pytorch.org\/ Acessado 8-Outubro-2024"},{"key":"30_CR27","unstructured":"Rapids: Rapids (2024). https:\/\/rapids.ai Acessado 8-Outubro-2024"},{"key":"30_CR28","doi-asserted-by":"publisher","unstructured":"Rehab, M.A., Boufares, F.: Scalable massively parallel learning of multiple linear regression algorithm with mapreduce. In: 2015 IEEE Trustcom\/BigDataSE\/ISPA, vol.\u00a02, pp. 41\u201347 (Aug 2015). https:\/\/doi.org\/10.1109\/Trustcom.2015.560","DOI":"10.1109\/Trustcom.2015.560"},{"key":"30_CR29","doi-asserted-by":"publisher","unstructured":"Sejnowski, T., Gorman, R.: Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5T01Q","DOI":"10.24432\/C5T01Q"},{"key":"30_CR30","unstructured":"Sun, Z., Fox, G.: Study on parallel svm based on mapreduce. In: International Conference on Parallel and Distributed Processing Techniques and Applications, pp. 16\u201319 (2012)"},{"key":"30_CR31","unstructured":"TensorFlow: Tensorflow (2024). https:\/\/www.tensorflow.org Acessado 8-Outubro-2024"},{"key":"30_CR32","unstructured":"Tommy Morris: Power system datasets (2014). https:\/\/sites.google.com\/a\/uah.edu\/tommy-morris-uah\/ics-data-sets Acessado 06-mar\u00e7o-2025"},{"key":"30_CR33","unstructured":"Torch-KMeans: Torch-kmeans (2022). https:\/\/pypi.org\/project\/torch-kmeans Acessado 12-Novembro-2024"},{"issue":"11","key":"30_CR34","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TPAMI.2008.128","volume":"30","author":"A Torralba","year":"2008","unstructured":"Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(11), 1958\u20131970 (2008). https:\/\/doi.org\/10.1109\/TPAMI.2008.128","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR35","unstructured":"Tyree, S., Gardner, J.R., Weinberger, K.Q., Agrawal, K., Tran, J.: Parallel Support Vector Machines in Practice arXiv: 1404.1066 (2014)"},{"key":"30_CR36","unstructured":"Uci datasets: Uci datasets (2011). https:\/\/archive.ics.uci.edu\/datasets Acessado 26-Novembro-2024"},{"key":"30_CR37","doi-asserted-by":"publisher","unstructured":"Virtanen, P., et al.: SciPy 1.0 contributors: SciPy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17, 261\u2013272 (2020). https:\/\/doi.org\/10.1038\/s41592-019-0686-2","DOI":"10.1038\/s41592-019-0686-2"},{"key":"30_CR38","unstructured":"Zhang, H., Si, S., Hsieh, C.J.: Gpu-acceleration for large-scale tree boosting. arXiv preprint arXiv:1706.08359 (2017)"},{"key":"30_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, J.P., Li, Z.W., Yang, J.: A parallel svm training algorithm on large-scale classification problems. In: 2005 International Conference on Machine Learning and Cybernetics, vol.\u00a03, pp. 1637\u20131641. IEEE (2005)","DOI":"10.1109\/ICMLC.2005.1527207"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15993-9_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T05:52:20Z","timestamp":1770443540000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15993-9_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159922","9783032159939"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15993-9_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"8 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","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":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}