Applying Artificial Intelligence Techniques for Optimizing Big Data Retrieval Performance

The traditional paradigm for records storage has been to depend on conventional databases and query techniques, but this paradigm is turning into an increasing number of old because the want to effectively shop and retrieve massive quantities of records maintains to grow. This paper outlines the consequences of an evaluation exploring the capability utility of artificial intelligence (AI) algorithms and techniques for optimizing records retrieval performance in big, dispensed information stores. Via the use of those algorithms, strategies including genetic algorithms and neural networks are explored for indexing, clustering, and records mining. Some of the strategies mentioned on this paper are complementary and may be used at the side of other question optimization techniques which includes those primarily based on indexes, statistical methods, and question optimizations. Using AI strategies can improve the velocity, accuracy, and scalability of huge data retrieval overall performance whilst presenting better effects and advanced efficiency.