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Existing cloud frameworks involve isolating low-level operations within an application for data distribution and partitioning. This limits their applicability for many applications with complex data dependency considerations. This paper aims to explore new methods of partitioning and distributing data in the cloud by fundamentally re-thinking the way in which future data management models will need to be developed on the Internet. Loosely-coupled associative computing techniques, which have so far not been considered, can provide the break-through needed for a distributed data management scheme. Using a novel lightweight associative memory algorithm known as Edge Detecting Hierarchical Graph Neuron (Edge HGN), data retrieval/processing can be modeled as a pattern recognition problem, conducted across multiple records within a single-cycle, utilizing a parallel approach. The proposed model envisions a distributed data management scheme for large-scale data processing and database updating that is capable of providing scalable real-time recognition and processing with high accuracy while being able to maintain low computational cost in its function.