Guest Editors
Dr. Muhammad Ashfaq Khan, Incheon National University, Korea.
Dr. B.S. Chowdhry, Mehran University of Engineering & Technology (MUET) Jamshoro, Pakistan.
Dr. Hoon Kim, Incheon National University, Korea.
Summary
Every day we experience an unprecedented amount of data growth from numerous sources, which contribute to big data. As these data is growing rapidly in terms of volume, velocity, and variability, these impose great challenges on analytics framework and computational resources, making the overall analysis difficult to extract meaningful information in a timely manner. Thus, to harness such kind of challenges, developing an efficient big data analytics framework is a non-trivial research. Machine learning (ML) and deep learning (DL) algorithms are being in use in such an analytics framework to address these challenges by exploiting non-linear relationships from very high-dimensional datasets.
Dataset size is increasing rapidly, and it is essential to develop tools capable of controlling data size and extracting meaningful information. Big data is now used in many aspects of science, technology, and commerce. This data is generated from many disparate sources, including online transactions, emails, audios, videos, social networking sites, etc. Every corporation or organization that produces these data needs to manage and analyze it Big data can be defined as massive data collections (real-time, non-structured, streaming) that make it complicated to utilize conventional data management and analysis methods to manage, analyze, store, and obtain meaningful information . To evaluate big data, it is essential to employ suitable analysis tools. This pressing need to control the increasingly huge amount of data has led to a major interest in designing suitable big data frameworks. Significant research has investigated various big data domains, e.g. infrastructure, management, data searching, mining, security, etc. Big data infrastructure has been developed to identify analytics to utilize quick, reliable, and versatile computational design, providing efficient quality attributes including flexibility, accessibility, and resource pooling with on-demand and ease-of-use.
Keywords
Big data, Artificial Intelligence; Deep learning; Machine learning, CNN, LSTM, Big data processing framework
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