Advances in Distributed and Parallel Big Data Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (30 January 2024) | Viewed by 1224
Special Issue Editor
Interests: deep learning and big data; biometric recognition; information security; cloud and fault computing; multimedia applications; medical applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Big data refers to a large-scale and complex collection of data that span various fields such as business, scientific research, healthcare, and finance, among others. Through big data analysis, hidden patterns and trends can be discovered, providing insights into market trends, user behavior, disease forecasting, and more.
To handle the scale and complexity of big data, parallel or distributed techniques are required. Traditional serial processing methods are inefficient for processing large datasets; thus, parallel and distributed computing have become crucial. Parallel computing involves dividing tasks into multiple sub-tasks and executing them simultaneously on multiple processing units. Distributed computing involves distributing data across multiple computing nodes, with each node responsible for processing a portion of the data, and communication and coordination occur over a network. These techniques leverage the computational and storage capabilities of multiple computers to enhance data processing speed and efficiency.
Big data require the adoption of parallel or distributed techniques to effectively handle their scale and complexity. These techniques enable the efficient processing of big data. The application prospects of big data are vast and will continue to have a significant impact across various domains.
We welcome new and unpublished papers on, but not limited to, the following topics:
- Mass data stream processing in the cloud;
- Big data models and computation theory;
- Big data mining and fusion;
- Dimension reduction for large data sets;
- Big data placement, scheduling and optimization;
- Multi-source data processing and integration;
- Deep learning models;
- Deep learning applications;
- Cloud security;
- Data privacy protection;
- Access control;
- Data provenance.
Prof. Dr. Shi-Jinn Horng
Guest Editor
Manuscript Submission Information
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Keywords
- distributed computing
- parallel computing
- big data
- data mining
- deep learning
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