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Updated: Oct 1, 2025
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
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QTT-DLSTM: A Cloud-Edge-Aided Distributed LSTM for Cyber-Physical-Social Big Data
Summary
This study introduces a novel cloud-edge AI method for analyzing complex Cyber-Physical-Social Systems (CPSS) big data. The Quantized Tensor-Train Distributed Long Short-Term Memory (QTT-DLSTM) efficiently processes multi-attribute data for personalized services.
Area of Science:
- Cross-disciplinary research integrating Cyber-Physical Systems (CPS) and social networking.
- Artificial Intelligence (AI) applications in data processing and analysis.
Background:
- Cyber-Physical-Social Systems (CPSS) generate vast amounts of big data essential for personalized services.
- Efficient processing of CPSS big data is crucial for extracting valuable insights.
- Edge computing offers real-time processing capabilities complementing cloud computing.
Purpose of the Study:
- To present a cloud-edge-aided Quantized Tensor-Train Distributed Long Short-Term Memory (QTT-DLSTM) method.
- To facilitate the multi-attribute processing and analysis of CPSS big data.
- To enhance the efficiency of CPSS data mining for personalized services.
Main Methods:
- Representing multi-attribute CPSS big data using tensors decomposed into Quantized Tensor-Train (QTT) form.
- Implementing a distributed cloud-edge computing model for systematic data processing.
- Employing a distributed computing strategy for efficient training by partitioning weight matrices and input data.
Main Results:
- The proposed QTT-DLSTM method demonstrates effective processing of multi-attribute CPSS big data.
- Distributed cloud-edge computing enhances the efficiency of large-scale and edge data processing.
- Experimental evaluation on diverse datasets validates the performance of the QTT-DLSTM approach.
Conclusions:
- The QTT-DLSTM method provides an efficient solution for analyzing CPSS big data.
- Cloud-edge computing architectures are vital for real-time and large-scale data analysis in CPSS.
- This approach facilitates the development of advanced personalized services within CPSS environments.
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