Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams

Suseendran, G.; Balaganesh, D.; Akila, D.; Pal, Souvik

doi:10.1109/icoa51614.2021.9442621

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…In [56], the authors concentrated on the mining of semistructured data in the data flow based on the deep learning (DL), semistructured tree miner algorithm. The data can be effectively mined and stored with the time attenuation model.…”

Section: Targetmentioning

confidence: 99%

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Luo¹,

Li²,

Yang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification over power big data into data acquisition and storage processes, anomaly detection and fault discrimination processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and future directions are outlined.

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Section: Targetmentioning

confidence: 99%