Abstract
Recent decades have shown tremendous growth in both hardware and software. Today, data is generated everywhere from schools, colleges, hospitals, institutions, industries, supermarkets, railway stations, traffic system, communication industry, and so on. Most of this data is generated through digital devices and electronic gadgets. This digital flooding paved the way for business analytics where the data can be analyzed on the business perspective to identify the needs and scope of the consumers and thereby to increase the profit margin. Particularly nowadays, streaming data is generated abundantly everywhere. Storing, processing, and analyzing stream data in real time are a major challenge today. Time-critical applications generate fast streams of temporal data. Analytics with stream data is meaningful only if there is quick and immediate response. Delayed response is of no use in the case of stream data analytics. This paper does an extensive study of the applications, analytic methods, and algorithms that can be applied on continuous streaming data to achieve better performance. A comparative analysis of the traditional, deep learning and reinforcement algorithms is also described. Finally, the challenges in handling stream data are analyzed and defined along with its future scope.
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Amudha, L., Pushpalakshmi, R. (2021). Applications, Analytics, and Algorithms—3 A’s of Stream Data: A Complete Survey. In: Peter, J., Fernandes, S., Alavi, A. (eds) Intelligence in Big Data Technologies—Beyond the Hype. Advances in Intelligent Systems and Computing, vol 1167. Springer, Singapore. https://doi.org/10.1007/978-981-15-5285-4_60
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DOI: https://doi.org/10.1007/978-981-15-5285-4_60
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