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IEEE Final Year Project Topic for CSE

Base Paper Title

A Multi-order Distributed HOSVD with its Incremental Computing for Big Services in Cyber-Physical-Social Systems

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IEEE Project Abstract

Big service is an extremely important application of service computing to provide predictive and needed services to humans. To operationalize big service, the heterogeneous data collected from Cyber-Physical-Social Systems (CPSS) must be processed efficiently. However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. Therefore, in this paper, we propose a multi-order distributed high-order singular-value decomposition method (MDHOSVD) with its incremental computational algorithm. To realize the MDHOSVD, a tensor blocks unfolding integration regulation is proposed. This method allows for the efficient analysis of large-scale heterogeneous data in blocks in an incremental fashion. Using simulation an experimental results, the high efficiency of the proposed data processing and computational method, is demonstrated. Further, a case study about cyber-physical-social systems data processing is illustrated. The proposed MDHOSVD method speeds up data processing, scales with data volume, improves the adaptability and extensibility over data diversity and converts low-level data into actionable knowledge.Big service is an extremely important application of service computing to provide predictive and needed services to humans. To operationalize big service, the heterogeneous data collected from Cyber-Physical-Social Systems (CPSS) must be processed efficiently. However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. Therefore, in this paper, we propose a multi-order distributed high-order singular-value decomposition method (MDHOSVD) with its incremental computational algorithm. To realize the MDHOSVD, a tensor blocks unfolding integration regulation is proposed. This method allows for the efficient analysis of large-scale heterogeneous data in blocks in an incremental fashion. Using simulation an experimental results, the high efficiency of the proposed data processing and computational method, is demonstrated. Further, a case study about cyber-physical-social systems data processing is illustrated. The proposed MDHOSVD method speeds up data processing, scales with data volume, improves the adaptability and extensibility over data diversity and converts low-level data into actionable knowledge.

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