Abstract
Data collection and processing in real time is one of the most challenging domains for big data. The sustainable proliferation of unbounded streaming data has become arduous for data collection, data pre-process, data optimization, etc. Real-time streaming for data collection can effectively be performed by windowing mechanism. In this communication, we have discussed various windowing mechanisms such as sliding window, tumbling window, landmark window, index-based window, adaptive size tumbling window, and partitioned-based window. The reliability measure, which depends upon selection of appropriate windowing mechanism, has also been discussed. These window-based algorithms have been compared on the basis of CPU utilization, memory consumption, time efficiency, and operation compatibility. In this paper, we have surveyed various aggregation algorithms such as reactive aggregator, flatFAT, flatFIT, B-Int, DABA, and two stacks aggregator and compared them based on time complexity. Remarkably, a hybrid window mechanism has been introduced in this study which can handle the most recent data stream and variable rate of data stream by sliding window and tumbling window, respectively.
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Acknowledgements
I offer most sincere gratitude to the Council of Scientific and Industrial Research (CSIR), Government of India, for financial support in the form of Junior Research Fellowships.
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Lal, D.K., Suman, U. (2020). A Survey of Real-Time Big Data Processing Algorithms. In: Gupta, V., Varde, P., Kankar, P., Joshi, N. (eds) Reliability and Risk Assessment in Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-3746-2_1
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