Artificial intelligence-based adaptive big data storage and retrieval optimization method and system thereof

A system for artificial intelligence-based adaptive big data storage and retrieval optimization, comprising:

• a data acquisition unit configured to receive and preprocess structured, semi-structured, and unstructured data streams from multiple heterogeneous data sources, the data acquisition unit including a data normalization circuit and a format harmonization processor for converting incoming data into a unified intermediate representation;
• a distributed storage unit comprising a plurality of interconnected storage nodes, each storage node including a memory unit, a cache memory array, a data indexing processor, and a node control logic circuit, the distributed storage unit being configured to store data fragments in a tiered storage hierarchy comprising high-speed volatile and non-volatile memory layers;
• a metadata processing unit coupled to the distributed storage unit, the metadata processing unit including a graph-based indexing processor and a relational mapping controller configured to generate multi-dimensional metadata representations comprising semantic, temporal, and structural attributes of the stored datasets;
• an adaptive learning unit comprising a neural computation processor, a reinforcement learning controller, and a feature extraction array, the adaptive learning unit being configured to receive performance telemetry data from the distributed storage unit, analyze historical access patterns, predict optimal data placement configurations, and generate adaptive control parameters for optimizing storage and retrieval operations;
• a retrieval optimization unit coupled to the metadata processing unit and the adaptive learning unit, the retrieval optimization unit including a semantic query interpreter, a latency prediction processor, and a dynamic routing controller configured to select the optimal data retrieval pathway and node cluster for execution of incoming user queries;
• and
• a central coordination controller operatively coupled to the data acquisition unit, the distributed storage unit, the metadata processing unit, the adaptive learning unit, and the retrieval optimization unit, the central coordination controller including a synchronization processor, a decision arbitration logic circuit, and a high-speed interconnect interface configured to coordinate communication, maintain consistency of metadata, and implement adaptive configuration updates across the distributed storage environment;
• wherein the adaptive learning unit performs optimization of data placement by receiving continuous telemetry streams from the distributed storage unit including block access frequencies, read/write contention counts, and cache invalidation events, converting such telemetry into normalized numerical tensors through the feature extraction array, and feeding said tensors into the neural computation processor configured to compute gradient adjustments through backpropagation;
• wherein the reinforcement learning controller interprets the resulting weight differentials as updated control parameters, generates node-specific migration directives, and communicates these directives through the central coordination controller to the node control logic circuits, which execute fragment relocation and tier promotion or demotion operations according to the received adaptive control parameters;
• and wherein the metadata processing unit updates its graph-based metadata representations by monitoring modification logs generated by the distributed storage unit, parsing these logs to detect insertions, deletions, and relocation operations, recalculating node-to-node relational weights through iterative propagation within the graph-based indexing processor, and maintaining synchronization with the distributed metadata copies by transmitting version vectors through the synchronization processor of the central coordination controller;
• wherein conflict resolution between concurrent metadata versions is achieved through dependency timestamp arbitration controlled by the decision arbitration logic circuit to preserve ordering consistency across the distributed environment;
• and wherein the retrieval optimization unit determines the optimal retrieval path by first analyzing the semantic structure of an incoming query through token segmentation and contextual embedding performed by the semantic query interpreter, mapping each identified semantic token to metadata entries stored within the metadata processing unit, and then evaluating expected response latencies using predictive functions computed by the latency prediction processor;
• wherein the dynamic routing controller selects the most efficient retrieval node cluster by comparing estimated completion times, issuing routing instructions through the high-speed interconnect interface to the distributed storage unit, and continuously updating its internal latency models based on feedback data obtained from the completion reports of executed retrieval operations.