Optimizing Distributed Database Data Compression using AI Algorithms
Keywords:
data compression, distributed databases, AI algorithmsAbstract
To swiftly store and retrieve large volumes of data, distributed databases compress. AI algorithms are studied for condensing database data. AI-enhanced compression affects storage efficiency, compression ratios, access speed, and scalability in this case study. The case study contrasts deep learning, reinforcement learning, Huffman coding, LZW, and compression methods. The study shows that AI systems can adapt to shifting data patterns and enhance distributed database performance. Method complexity, training data, and processing overhead are examined. Evaluate and implement AI-powered distributed database compression.
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