Abstract
The outburst of Bigdata has driven a great deal of research to build and extend systems for in-memory data analytics in real-time environment. Stream data mining makes allocation of tasks efficient among various distributed computational resources. Managing chunk of unbounded stream data is challenging task as data ranges from structured to unstructured. Beyond size, it is heterogeneous and dynamic in nature. Scalability and low-latency outputs are vital while dealing with big stream data. The potentiality of traditional approach like data stream management systems (DSMSs) is inadequate to ingest and process huge volume and unbounded stream data for knowledge extraction. A novel approach develops architectures, algorithms, and tools for uninterrupted querying over big stream data in real-time environment. This paper overviews various challenges and approaches related to big stream data mining. In addition, this paper surveys various platforms and proposed framework which can be applied to near-real- or real-time applications.
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References
Gantz, J., Reinsel, D.: The Digital Universe in 2020–Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East, IDC IView (2016)
Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 (2007)
Mitra, S., Pal, S.K., Mitra, P.: Data mining in soft computing framework: a survey. IEEE Trans. Neural Netw. 13(1), 3–14 (2002)
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., Coello, C.A.C.: A survey of multiobjective evolutionary algorithms for data mining: part I. IEEE Trans. Evol. Comput. 18(1), 4–19 (2014)
Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning Publications Co. (2015)
Bello-Orgaz, G., Jung, J.J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: Recent advances and emerging challenges of feature selection in the context of big data. Knowl.-Based Syst. 86, 33–45 (2015)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: a review. ACM Sigmod Rec. 34(2), 18–26 (2005)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART symposium on Principles of Database Systems, pp. 1–16. ACM (2002)
Jiang, N., Gruenwald, L.: Research issues in data stream association rule mining. ACM Sigmod Rec. 35(1), 14–19 (2006)
SAS Institute Inc.: Five big data challenges and how to overcome them with visual analytics, Report, pp. 1–2 (2013)
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Tidke, B., Mehta, R.G., Dhanani, J. (2018). Real-Time Bigdata Analytics: A Stream Data Mining Approach. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 708. Springer, Singapore. https://doi.org/10.1007/978-981-10-8636-6_36
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DOI: https://doi.org/10.1007/978-981-10-8636-6_36
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