Computational Statistics in Data Science. Группа авторов
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55 55 Gao, F., Ali, M.I., Cury, E., and Mileo, A. (2017) Automated discovery and integration of semantic urban data streams: the ACEIS middleware. Futur. Gener. Comput. Syst., 76, 561–581.
56 56 Toll, W. (2014) Top 45 Big Data Tool for Developers, https://blog.profitbricks.com/top‐45‐big‐data‐tools‐for‐developers.
57 57 Baciu, G., Li, C., Wang, Y., and Zhang, X. (2015) Cloudet: a cloud‐driven visual cognition for large streaming data. Int. J. Cognitive Inform. Nat. Intel., 10 (1), 12–31. doi: 10.4018/IJCINI.2016010102.
58 58 Chen, X.J. and Ke, J. (2015) Fast Processing of Conversion Time Data Flow in Cloud Computing via Weighted FP‐Tree Mining Algorithms. Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conference on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conference on Scalable Computing and Communications and Its Associated Workshops (UIC‐ATC‐ScalCom), Beijing, China, pp. 386–391.
59 59 Chen, X., Chen, H., Zhang, N. et al. (2015) Large‐scale real‐time semantic processing framework for internet of things. Int. J. Distrib. Sens. Net., 11 (10), 365–372. doi: 10.1155/2015/365372.
60 60 Kropivnitskaya, Y., Qin, J., Tiampo, K.F., and Bauer, M.A. (2015) A pipelining implementation for high resolution seismic hazard maps production. Procedia Comput. Sci., 51, 1473–1482.
61 61 Birjali, M., Beni‐Hssane, A., and Erritali, M. (2017) Analyzing social media through big data using infosphere biginsights and apache flume. Procedia Comput. Sci., 113, 280–285. doi: 10.1016/j.procs.2017.08.299.
62 62 Warner, J (2019) 5 Streaming Analytics Platforms for All Real‐Time Applications, https://www.google.com/amp/s/datafloq.com/read/amp/streaming‐analytics‐platforms‐real‐time‐apps/4658.
63 63 Yang, H., Lee, Y., Lee, H. et al. (2015) A study on word vector models for representing Korean semantic information. Phone. Speech Sci., 7, 41–47. doi: 10.13064/KSSS.2015.7.4.041.
64 64 Joseph, S. and Jasmin, E.A. (2016) Stream Computing Framework for Outage Detection in Smart Grid. Proceedings of 2015 IEEE International Conference on Power Instrumentation, Control and Computing, Thrissur, India, pp. 1–5. doi: 10.1109/PICC.2015.7455744.
65 65 Barika, M., Garg, S., Chan, A. et al. (2019) IoTSim‐stream: modelling stream graph application in cloud simulation. Futur. Gener. Comput. Syst., 99, 86–105.
66 66 Ramírez‐Gallego, S., Krawczyk, B., García, S., and Woniak, M. (2017) A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing, 239, 39–57. doi: 10.1016/j.neucom.2017.01.078.
67 67 Kolajo, T., Daramola, O., Adebiyi, A., and Seth, A. (2020) A framework for pre‐processing of social media feeds based on local knowledge base. Inf. Process. Manag., 57 (6), 102348.
68 68 Gill, S. and Lee, B. (2015) A framework for distributed cleaning of data streams. Procedia Comput. Sci., 52, 1186–1191.
69 69 Ramírez‐Gallego, S., García, S., and Herrera, F. (2018) Online entropy‐based discretization for data streaming classification. Future Gener. Comp. Syst., 86, 59–70. doi: 10.1016/j.future.2018.03.008.
70 70 Herrera, F., Charte, F., Rivera, A.J., and del Jesús, M.J. (2016) Multi‐Label Classification – Problem Analysis, Metrics and Techniques, 1st edn, Springer, Cham.
71 71 Krawczyk, B. (2016) GPU‐accelerated extreme learning machines for imbalanced data streams with concept drift. Procedia Comput. Sci., 80, 1692–1701.
72 72 Herrera, F., Ventura, S., Bello, R. et al. (2016) Multiple Instance Learning – Foundations and Algorithms, Cham, Switzerland Springer.
73 73 García, S., Ramírez‐Gallego, S., Luengo, J. et al. (2016) Big data preprocessing: methods and prospects. Big Data Anal., 1, 9. doi: 10.1186/s41044‐016‐0014‐0.
74 74 Hasan, M., Orgun, M.A., and Schwitter, R. (2019) Real‐time event detection from the twitter data stream using the twitterNews + framework. Inf. Process. Manag., 56 (3), 1146–1165.
75 75 Pagliardini, M., Gupta, P., and Jaggi, M. (2018) Unsupervised Learning of Sentence Embeddings using Compositional n‐Gram Features. Proceedings of NAACL‐HLT. ACM, New Orleans, LA, USA, pp. 528–540.
76 76 Wu, L., Morstatter, F., and Liu, H. (2018) SlangSD: building, expanding and using a sentiment dictionary of slang words for short‐text sentiment classification. Lang Res. Eval., 52 (3), 839–852. doi: 10.1007/s10579‐018‐9416‐0.
77 77 Wankhede, S., Patil, R., Sonawane, S., and Save, A. (2018) Data Pre‐Processing for Efficient Sentimental Analysis. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, pp. 723–726.
78 78 Gupta, A., Taneja, S.B., Malik, G. et al. (2019) SLANGZY: a fuzzy logic‐based algorithm for english slang meaning selection. Prog. Artif. Intell., 8, 111–121. doi: 10.1007/s13748‐018‐0159‐3.
79 79 Mehta, J.S. (2017) Concept drift in streaming data classification: algorithms, platforms and issues. Procedia Comput. Sci., 122, 804–811.
80 80 BakshiRohit, P. and Agarwal, S. (2016) Stream data mining: platforms, algorithms, performance evaluators and research trends. Int. J. Database Theory App., 9 (9), 201–218.
81 81 Wei, X., Liu, Y., and Wanga, X. (2019) A survey on quality‐assurance approximate stream processing and applications. Futur. Gener. Comput. Syst., 101, 1062–1080.
82 82 Hu, Y., Jiang, Z., Zhan, P. et al. (2018) A novel multi‐resolution representation for streaming time series. Procedia Comput. Sci., 129, 178–184. doi: 10.1016/j.procs.2018.03.069.
83 83 Yaseen, M.U., Anjum, A., Rana, O., and Hill, R. (2018) Cloud‐based scalable object detection and classification in video streams. Futur. Gener. Comput. Syst., 80, 286–298. doi: 10.1016/j.future.2017.02.003.
84 84 Boushaki, S.I., Kamel, N., and Bendjeghaba, O. (2018) High‐dimensional text datasets clustering algorithm based on cuckoo search and latent semantic indexing. J. Inf. Knowl. Manag., 17 (3), 1–24.
85 85 Neto, J.M., Severiano Junior, C.A., Guimarães, F.G. et al. (2020) Evolving clustering algorithm based on mixture of typicalities for stream. Futur. Gener. Comput. Syst., 106, 672–684.
86 86 Ibrahim, O.A., Du, Y., and Keller, J.M. (2018) Extended robust online streaming clustering (EROLSC), in Information Processing and Management of Uncertainty in Knowledge‐Based Systems: Theory and Foundations (eds J. Medina et al.), Springer, Cadiz.
87 87 Sharma, N., Masih, S., and Makhija, P. (2018) A survey on clustering algorithms for data streams. Int. J. Comput. Appl., 182 (22), 18–24.
88 88 Panagiotou, N., Katakis, I., and Gunopulos, D. (2016) Detecting events in online social networks: definitions, trends and challenges, in Solving Large Scale Learning Tasks: Challenges and Algorithms (ed. S. Michaelis), Springer, Cham, pp. 42–84.
89 89 Li, Y., Guo, L., and Zhou, Z. (2019) Towards safe weakly supervised