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Research Topic Ideas in Deep Learning for Big Data Analytics

Research Topic Ideas in Deep Learning for Big Data Analytics

  The primary objective of big data analytics is to extract useful patterns from the huge amount of data that can be used in decision-making and prediction. High storage capacities, high computation time, and an increase in accessibility of massive amounts of data are the reason for the rise of big data analytics. Deep learning plays a powerful role in big data analytic solutions, which automatically extract complex features at a high level of abstraction from a large volume of data. The deep learning model can handle large amounts of data, real-time data, heterogeneous data, and low-quality data, characteristics of big data feature learning.

  In big data analytics, Deep learning algorithms process data in real-time with high accuracy and efficiency. It uses supervised/unsupervised techniques to learn and extract data representations automatically. The typical deep learning models for big data analytics are deep belief networks, stack autoencoders, recurrent neural networks, and convolution neural networks. Several popular application areas of Big Data Analytics include business, public administration, national security, scientific research, healthcare, the Internet of Things (IoT), commercial recommendations, and stock exchanges. Future advancements for big data analytics in deep learning are Noisy or incomplete big data handling using deep learning, compressed large-scale deep learning models for big data learning, deep computational models with less complexity, optimization in a neural network for incremental learning in high-velocity data, to name a few.

  • The core of big data analytics is mining and extracting meaningful patterns from massive input data for decision making and prediction.

  • Deep Learning algorithms strive to emulate the hierarchical learning approach of the human brain and utilize a huge amount of unsupervised data to extract complex representations automatically.

  • A key benefit of deep learning is big data analytics as extracting high-level patterns from massive volumes of data through the hierarchical learning process, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks.

  • Though, big data analytics poses various challenges in deep learning and data analysis, including format variation of the raw data, fast-moving streaming data, the trustworthiness of the data analysis, highly distributed input sources, noisy and poor quality data, high dimensionality, scalability of algorithms, imbalanced input data, unsupervised and un-categorized data, limited supervised/labeled data.

  • Even though deep learning models have made great strides in big data analysis; however, their performances are not ideal on small or unbalanced datasets.

  • Moreover, it demands some further research involving data sampling for generating useful high-level abstractions, domain (data distribution) adaption, defining criteria for extracting good data representations for discriminative and indexing tasks, semi-supervised learning, and active learning.