{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:18:56Z","timestamp":1764775136499,"version":"3.46.0"},"reference-count":58,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Intelligent learning applied to multidimensional data streams has established itself as a rapidly expanding field, driven by the growth of ubiquitous computing and the Internet of Things. The complexity of these streams, characterized by their high dimensionality, variability, and continuous nature, poses significant challenges for traditional approaches to analysis. This study presents a bibliometric analysis of scientific output indexed in Scopus between 2015 and 2025, with the aim of identifying trends, challenges, and opportunities in this field. The results show sustained growth in publications, a marked interdisciplinary orientation, and a diversity of applications including transportation, biomedicine, energy, and information systems. Likewise, there is a geographical concentration in certain leading countries and uneven development in terms of international collaboration. This work contributes to mapping the current state of the field and points to future lines of research aimed at its consolidation.<\/jats:p>","DOI":"10.3390\/info16121067","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:02:48Z","timestamp":1764774168000},"page":"1067","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Learning on Multidimensional Data Streams: A Bibliometric Analysis of Research Evolution and Future Directions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3711-1906","authenticated-orcid":false,"given":"Gary","family":"Reyes","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Group, Universidad Bolivariana del Ecuador, Campus Dur\u00e1n Km 5.5 v\u00eda Dur\u00e1n Yaguachi, Dur\u00e1n 092405, Ecuador"},{"name":"Facultad de Ciencias Matem\u00e1ticas y F\u00edsicas, Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil 090514, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4164-5839","authenticated-orcid":false,"given":"Roberto","family":"Tolozano-Benites","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Group, Universidad Bolivariana del Ecuador, Campus Dur\u00e1n Km 5.5 v\u00eda Dur\u00e1n Yaguachi, Dur\u00e1n 092405, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7027-7564","authenticated-orcid":false,"given":"Laura","family":"Lanzarini","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n en Inform\u00e1tica LIDI (Centro CICPBA), Facultad de Inform\u00e1tica, Universidad Nacional de La Plata, Buenos Aires CP1900, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9950-1563","authenticated-orcid":false,"given":"Waldo","family":"Hasperu\u00e9","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n en Inform\u00e1tica LIDI (Centro CICPBA), Facultad de Inform\u00e1tica, Universidad Nacional de La Plata, Buenos Aires CP1900, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2732-979X","authenticated-orcid":false,"given":"Julio","family":"Barzola-Monteses","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Group, Universidad Bolivariana del Ecuador, Campus Dur\u00e1n Km 5.5 v\u00eda Dur\u00e1n Yaguachi, Dur\u00e1n 092405, Ecuador"},{"name":"Facultad de Ciencias Matem\u00e1ticas y F\u00edsicas, Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil 090514, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/MIS.2024.3479469","article-title":"Streaming Continual Learning for Unified Adaptive Intelligence in Dynamic Environments","volume":"39","author":"Giannini","year":"2024","journal-title":"IEEE Intell. Syst."},{"key":"ref_2","unstructured":"Omranpour, S., Rabusseau, G., and Rabbany, R. (2024). Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data. arXiv."},{"key":"ref_3","unstructured":"Pic\u00f3n, G.C., Oleksiienko, I., Hedegaard, L., Bakhtiarnia, A., and Iosifidis, A. (2024). Continual Low-Rank Scaled Dot-product Attention. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4746","DOI":"10.14778\/3704965.3704980","article-title":"TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time Ranges","volume":"17","author":"Qiu","year":"2024","journal-title":"Proc. VLDB Endow."},{"key":"ref_5","unstructured":"Lanzarini, L.C., Hasperu\u00e9, W., Villa Monte, A., Jimbo Santana, P., Reyes Zambrano, G., Corvi, J.P., Fern\u00e1ndez Bariviera, A., and Olivas Varela, J.\u00c1. (2019, January 25\u201326). Miner\u00eda de Datos, Miner\u00eda de Textos y Big Data. Proceedings of the XXI Workshop de Investigadores En Ciencias de La Computaci\u00f3n (WICC 2019, Universidad Nacional de San Juan), San Juan, Argentina."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shu, H., Li, J., Jin, Y., and Wang, H. (2025). Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory. arXiv.","DOI":"10.1109\/TSP.2025.3632844"},{"key":"ref_7","unstructured":"Hou, Y., and Tang, P. (2025). Multi-Head Self-Attending Neural Tucker Factorization. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.ribaf.2019.06.008","article-title":"A Bibliometric Analysis of Bitcoin Scientific Production","volume":"50","author":"Bariviera","year":"2019","journal-title":"Res. Int. Bus. Financ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Haddow, G. (2018). Bibliometric Research. Research Methods, Elsevier. [2nd ed.]. Chapter 10.","DOI":"10.1016\/B978-0-08-102220-7.00010-8"},{"key":"ref_10","first-page":"1","article-title":"Mapping and Performance Evaluation of Mathematics Education Research in Turkey: A Bibliometric Analysis from 2005 to 2021","volume":"6","author":"Dede","year":"2022","journal-title":"J. Pedagog. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1108\/IJQRM-06-2022-0181","article-title":"A Bibliometric Analysis of IJQRM Journal (2002\u20132022)","volume":"40","author":"Singh","year":"2023","journal-title":"Int. J. Qual. Reliab. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., and Huang, Y. (2010, January 2\u20135). T-Drive: Driving Directions Based on Taxi Trajectories. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS \u201910), San Jose, CA, USA.","DOI":"10.1145\/1869790.1869807"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, Z., He, Y., Wu, D., Zuo, L., Li, K., Zhang, W., and Deng, Z. (2024, January 15\u201318). \u21131,2 -Norm and CUR Decomposition Based Sparse Online Active Learning for Data Streams with Streaming Features. Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA.","DOI":"10.1109\/BigData62323.2024.10825278"},{"key":"ref_14","first-page":"175","article-title":"Reference Architecture for an Intelligent Transportation System","volume":"15","author":"Zambrano","year":"2016","journal-title":"Int. J. Innov. Appl. Stud."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mutambik, I. (2024). An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams. Sensors, 24.","DOI":"10.3390\/s24227412"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, F., Wu, D., Yang, J., and He, Y. (2022). An Online Sparse Streaming Feature Selection Algorithm. arXiv.","DOI":"10.1109\/ICNSC55942.2022.10004194"},{"key":"ref_17","first-page":"16356","article-title":"Anti-Drifting Feature Selection via Deep Reinforcement Learning (Student Abstract)","volume":"37","author":"Wang","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_18","unstructured":"Lu, C., Shi, L., Chen, Z., Wu, C., and Wierman, A. (2024). Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Sun, Y., and Shasha, D. (2022). Forgetful Forests: High Performance Learning Data Structures for Streaming Data under Concept Drift. arXiv.","DOI":"10.3390\/a16060278"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1145\/2523813","article-title":"A Survey on Concept Drift Adaptation","volume":"46","author":"Gama","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_21","first-page":"91","article-title":"An Overview of Concept Drift Applications","volume":"Volume 16","author":"Japkowicz","year":"2016","journal-title":"Big Data Analysis: New Algorithms for a New Society"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1090\/conm\/026\/737400","article-title":"Extensions of Lipschitz Mappings into a Hilbert Space","volume":"Volume 26","author":"Beals","year":"1984","journal-title":"Contemporary Mathematics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1177\/03611981211058429","article-title":"Proposal for a Pivot-Based Vehicle Trajectory Clustering Method","volume":"2676","author":"Reyes","year":"2022","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MCI.2015.2471196","article-title":"Learning in Nonstationary Environments: A Survey","volume":"10","author":"Ditzler","year":"2015","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/1083784.1083789","article-title":"Mining Data Streams: A Review","volume":"34","author":"Gaber","year":"2005","journal-title":"ACM SIGMOD Rec."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MIC.2008.87","article-title":"Semantic Sensor Web","volume":"12","author":"Sheth","year":"2008","journal-title":"IEEE Internet Comput."},{"key":"ref_27","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.comnet.2010.05.010","article-title":"The Internet of Things: A Survey","volume":"54","author":"Atzori","year":"2010","journal-title":"Comput. Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","article-title":"Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions","volume":"29","author":"Gubbi","year":"2013","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012, January 17). Fog Computing and Its Role in the Internet of Things. Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland.","DOI":"10.1145\/2342509.2342513"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e11","DOI":"10.24215\/16666038.22.e11","article-title":"Dynamic Grouping of Vehicle Trajectories","volume":"22","author":"Reyes","year":"2022","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at Scale","volume":"72","author":"Taylor","year":"2018","journal-title":"Am. Stat."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0079-7421(08)60536-8","article-title":"Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem","volume":"Volume 24","author":"McCloskey","year":"1989","journal-title":"Psychology of Learning and Motivation"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming Catastrophic Forgetting in Neural Networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Reyes, G., Estrada, V., Tolozano-Benites, R., and Maquil\u00f3n, V. (2023). Batch Simplification Algorithm for Trajectories over Road Networks. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.20944\/preprints202308.0069.v1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor Decompositions and Applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF02289464","article-title":"Some Mathematical Notes on Three-Mode Factor Analysis","volume":"31","author":"Tucker","year":"1966","journal-title":"Psychometrika"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust Face Recognition via Sparse Representation","volume":"31","author":"Wright","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jouppi, N.P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., and Borchers, A. (2017, January 24\u201328). In-Datacenter Performance Analysis of a Tensor Processing Unit. Proceedings of the 44th Annual International Symposium on Computer Architecture, Toronto, ON, Canada.","DOI":"10.1145\/3079856.3080246"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the Mean Accuracy of Statistical Pattern Recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No Free Lunch Theorems for Optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_43","unstructured":"Shor, P. (1994, January 20\u201322). Algorithms for Quantum Computation: Discrete Logarithms and Factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science, Santa Fe, NM, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1109\/5.58356","article-title":"Neuromorphic Electronic Systems","volume":"78","author":"Mead","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping","volume":"84","author":"Waltman","year":"2010","journal-title":"Scientometrics"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3346","DOI":"10.1021\/acssensors.0c01424","article-title":"Advancing Biosensors with Machine Learning","volume":"5","author":"Cui","year":"2020","journal-title":"ACS Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/TII.2021.3095506","article-title":"Secure and Efficient Federated Learning for Smart Grid with Edge-Cloud Collaboration","volume":"18","author":"Su","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1016\/j.matt.2022.02.016","article-title":"A High-Accuracy, Real-Time, Intelligent Material Perception System with a Machine-Learning-Motivated Pressure-Sensitive Electronic Skin","volume":"5","author":"Wei","year":"2022","journal-title":"Matter"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.promfg.2017.04.039","article-title":"Digital Twin as Enabler for an Innovative Digital Shopfloor Management System in the ESB Logistics Learning Factory at Reutlingen-University","volume":"9","author":"Brenner","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.trc.2019.03.003","article-title":"Missing Traffic Data Imputation and Pattern Discovery with a Bayesian Augmented Tensor Factorization Model","volume":"104","author":"Chen","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s12206-022-0102-1","article-title":"Application of Recurrent Neural Network to Mechanical Fault Diagnosis: A Review","volume":"36","author":"Zhu","year":"2022","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1016\/j.matt.2020.08.034","article-title":"Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine","volume":"3","author":"Galan","year":"2020","journal-title":"Matter"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102622","DOI":"10.1016\/j.trc.2020.102622","article-title":"A Variational Autoencoder Solution for Road Traffic Forecasting Systems: Missing Data Imputation, Dimension Reduction, Model Selection and Anomaly Detection","volume":"115","author":"Boquet","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1080\/09588221.2019.1595664","article-title":"Intelligent Personal Assistants: Can They Understand and Be Understood by Accented L2 Learners?","volume":"33","author":"Moussalli","year":"2020","journal-title":"Comput. Assist. Lang. Learn."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Princy, R.J.P., Parthasarathy, S., Hency Jose, P.S., Raj Lakshminarayanan, A., and Jeganathan, S. (2020, January 13\u201315). Prediction of Cardiac Disease Using Supervised Machine Learning Algorithms. Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS48265.2020.9121169"},{"key":"ref_56","first-page":"146","article-title":"An Approach for Detecting, Quantifying, and Visualizing the Evolution of a Research Field: A Practical Application to the Fuzzy Sets Theory Field","volume":"5","author":"Cobo","year":"2011","journal-title":"J. Inf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_58","first-page":"317","article-title":"The Frequency Distribution of Scientific Productivity","volume":"16","author":"Lotka","year":"1926","journal-title":"J. Wash. Acad. Sci."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1067\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:14:32Z","timestamp":1764774872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"references-count":58,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["info16121067"],"URL":"https:\/\/doi.org\/10.3390\/info16121067","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,3]]}}}