An Effective Model for Handling the Big Data Streams Based on the Optimization-Enabled Spark Framework

Srivani, B.; Sandhya, N.; Rani, B. Padmaja

doi:10.1007/978-981-15-5400-1_65

Cited by 6 publications

(1 citation statement)

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“…• The authors propose an incremental learning framework for big data stream using ant lion fuzzy-generative adversarial network model (ALF-GAN) that provide speed, high efficiency, good convergence, and eliminates local optima. • The proposed model is carried out in spark architecture that provides high scalability [30], in which master node and slave nodes are considered [31]. • The authors use a tri-model for the feature extraction process, which includes token keyword set, semantic keyword set, and contextual keyword set.…”

Section: Introductionmentioning

confidence: 99%

Incremental Learning Framework for Mining Big Data Stream

Eisa¹,

El-Rashidy²,

Alshehri³

et al. 2022

Computers, Materials &Amp; Continua

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At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model. The proposed model is implemented in spark architecture. For each data stream, the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation. This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency. The results show that the proposed model outperforms stateof-the-art performance in terms of accuracy (0.861) precision (0.9328) and minimal MSE (0.0416).

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Section: Introductionmentioning

confidence: 99%