A split-path schema-based RFID data storage model in supply chain management.

Area of Science:

• Supply Chain Management
• Information Systems
• Data Management

Background:

• Radio Frequency Identification (RFID) technology is crucial in modern supply chains, generating massive datasets.
• Existing RFID data management approaches struggle with the storage and processing demands of large datasets.
• Efficiently managing RFID data is essential for optimizing supply chain operations.

Purpose of the Study:

• To propose a novel RFID data storage model for enhanced efficiency in supply chain management.
• To address the limitations of current methods in handling large-scale RFID data.
• To improve both data expression and query performance for RFID data.

Main Methods:

• Development of a split-path schema-based RFID data storage model.
• Implementation of a data separation mechanism for efficient storage and processing.
• Introduction of a tree structure-based approach for intelligent product movement path splitting.
• Design of a relational schema for storing tag path and time information.
• Definition of query templates and SQL statements for data retrieval.

Main Results:

• The proposed model demonstrates significantly improved data expression capabilities.
• Experimental results show superior performance in path-oriented RFID data queries compared to baseline approaches.
• The data separation mechanism enhances the efficiency of storing and processing massive RFID datasets.

Conclusions:

• The split-path schema-based RFID data storage model offers a more efficient solution for managing large RFID datasets in supply chains.
• The proposed methods effectively address the challenges associated with storing and querying complex RFID data.
• This approach provides a foundation for more advanced RFID data management systems in logistics and operations.