Big data analysis techniques for image warehouse architecture

With the rapid growth of image data in recent years, efficient management and retrieval of image data have become increasingly important. In this paper, we propose an image warehouse architecture in the era of big data that combines data preprocessing, compression and deduplication, distributed processing and parallel computing, machine learning, and deep learning, and security and privacy techniques to improve the efficiency and scalability of image warehouses. We conducted experiments on a large-scale image dataset, and the results show that our approach significantly outperforms existing methods in terms of retrieval accuracy and efficiency. The proposed architecture provides a promising solution for managing and retrieving large-scale image data in the era of big data