survey, opportunities, and challenges. J. Big Data, 6, 44. doi: 10.1186/s40537‐019‐0206‐3.
40 40 Millman, N. (2014) Analytics for Business. Computerworld, https://www.computerworld.com/article/24758 40/bigdata/8‐considerations‐when‐selecting‐big‐data‐technology.html.
41 41 Brook, C. (2014) Enterprise NoSQL for Dummies, John Wiley & Sons, Hoboken.
42 42 Shanahan, J.G. and Dai, L. (2015) Large Scale Distributed Data Science using Apache Spark. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, pp. 2323–2324. doi: 10.1145/2783258.2789993.
43 43 Sharma, S. (2016) Expanded cloud plumes hiding big data ecosystem. Futur. Gener. Comput. Syst., 59, 63–92.
44 44 Meng, X., Bradley, J., Yavuz, B. et al. (2016) Mllib: machine learning in apache spark. J. Mach. Learn. Res., 17 (1), 1235–1241.
45 45 Mazumder, S. (2016) Big data application in engineering and science, in Big Data Concepts, Theories, and Applications (eds S. Yu and S. Guo), Springer, Cham, pp. 29–128. doi: 10.1007/978‐3‐319‐27763‐9_2.
46 46 Liao, X., Gao, Z., Ji, W., and Wang, Y. (2016) An Enforcement of Real‐Time Scheduling in Spark Streaming. Sixth IEEE International Green Computing Conference and Sustainable Computing Conference (IGSC). IEEE, Las Vegas, pp. 1–6. doi: 10.1109/IGCC.2015.7393730.
47 47 Jayanthi, D. and Sumathi, G. (2016) A Framework for Real‐Time Streaming Analytics Using Machine Learning Approach. Proceedings of the National Conference on Communication and Informatics, Sriperumbudur, India, pp. 85–90.
48 48 Agha, G. (1986) Actors: A Model of Concurrent Computation in Distributed Systems, MIT Press, Cambridge.
49 49 Ananthanarayanan, R., Basker, V., Das, S. et al. (2013). Photon: Fault‐Tolerant and Scalable Joining of Continuous Data Streams. Proceedings of 2013 ACM SIGMOD International Conference on Management of Data. ACM, New York, pp. 577–588. doi: 10.1145/2463676.2465272.
50 50 Apache Software Foundation (2017) Apache Aurora: System Overview, http://aurora.apache.org/documentation/latest/getting‐started/overview.
51 51 Yang, W., DaSilva, A., and Picard, M.L. (2015) Computing data quality indicators on big data streams using a CEP, in 2015 IEEE International Workshop on Computational Intelligence for Multimedia Understanding, IEEE, Prague, pp. 1–5.
52 52 Morales, F.G. (2013) SAMOA: A Platform for Mining Big Data Streams. Proceedings of the 22nd International Conference on World Wide Web. ACM, Rio de Janeiro, pp. 777–778.
53 53 Ren, X., Khrouf, H., Kazi‐Aoul, Z. et al. (2018) On Measuring Performances of C‐SPARQL and CQELS, Kobe, Japan https://hal‐upec‐upem.archives‐ouvertes.fr/hal‐01740520.
54 54 Keeney, J., Fallon, L., Tai, W., and O'Sullivan, D. (2015) Towards Composite Semantic Reasoning for Real‐Time Network Management Data Enrichment. Proceedings of the 2015 IEEE 11th International Conference on Network and Service Management (CNSM), Barcelona. pp. 246–250. doi: 10.1109/CNSM.2015.7367365.
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.
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