A hybrid deep learning classifier and Optimized Key Windowing approach for drift detection and adaption

Talapula, Dharani Kumar; Kumar, Adarsh; Ravulakollu, Kiran Kumar; Kumar, Manoj

doi:10.1016/j.dajour.2023.100178

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…Te performance of the algorithm was tested on real-world and synthetic datasets and showed a good performance. A hybrid deep learning classifer was used in [41] to detect the concept drifts in streaming data. Te proposed approach is able to handle the time and memory constraints.…”

Section: Literature Review and Analysismentioning

confidence: 99%

Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements

Alzghoul

2023

Advances in Operations Research

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Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring systems which have the ability to search big data streams. In this paper, the research works that discuss the analysis of online data streams for fault detection in industry are reviewed. Based on the literature analysis, the industrial needs and challenges of monitoring big data streams are presented. Furthermore, feasible suggestions for improving the real-time monitoring system are proposed.

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Section: Literature Review and Analysismentioning

confidence: 99%