A survey on deep learning for big data

By Qingchen Zhang, Laurence Tianruo Yang, Zhikui Chen, Peng Li. 2018

726 citations recorded by the source corpus.

Abstract Deep learning, as one of the most currently remarkable machine learning techniques, has achieved great success in many applications such as image analysis, speech recognition and text understanding. It uses supervised and unsupervised strategies to learn multi-level representations and features in hierarchical architectures for the tasks of classification and pattern recognition. Recent development in sensor networks and communication technologies has enabled the collection of big data. Although big data provides great opportunities for a broad of areas including e-commerce, industrial control and smart medical, it poses many challenging issues on data mining and information processing due to its characteristics of large volume, large variety, large velocity and large veracity. In the past few years, deep learning has played an important role in big data analytic solutions. In this paper, we review the emerging researches of deep learning models for big data feature learning. Furthermore, we point out the remaining challenges of big data deep learning and discuss the future topics.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “In this context, we discuss two research trends on applying machine learning techniques in data fusion as follows: 1) Explainable Deep Neural Network: Lately, supervised machine learning techniques focus on the DNN, where the in-depth reviews of the recent development can be found in [255]–[257].”
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “AEs are also scalable to diverse applications with big data, where the stacking of networks acquires notable importance [151].”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “Big Data Analytics Efficient Machine Learning for Big Data: A Review [273] A survey on deep learning for big data [274] A Survey on Data Collection for Machine Learning: a Big Data - AI”
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, background): “, 2017) Introducing typical DL architectures and their applications (Zhang et al., 2018) Reviewing DL models and their applications in analysising big data”
The use of Big Data Analytics in healthcare (2022, background): “In the healthcare sector, Big Data Analytics is expected to improve the quality of life and reduce operational costs [72, 82].”
A Survey on Deep Learning for Multimodal Data Fusion (2020, background): “Deep belief net RBM (Zhang et al., 2018) A generative graphic model that uses the energy to capture the probability distribution between visible units and hidden units.”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, methodology): “[170] - - - - - - - - - - - X - 500 samples for dataset Anomaly detection”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, positive): “Machine learning (ML) and deep learning (DL) approaches have been a predominant choice for various disease detection (Zhang, Yang, Chen, & Li, 2018).”