Distributed Data Processing Using Local Computational Frameworks

Instead of moving data to a centralized site for analysis, the patent inverts the approach by bringing the computational framework to the distributed data locations. Each data zone runs its own computational framework locally, performing analytics in-place without requiring data transfer to a central location.

The patent segments the computational framework into distributed instances that can operate independently across multiple data zones. Each zone maintains its own framework instance, enabling parallel processing of data locally while contributing to global analytics results through coordinated computation.

The patent extracts the file system dependency from the distributed processing architecture. Instead of requiring a shared distributed file system, each data zone maintains its own local storage, and the computational framework operates independently on local data without needing centralized file system management.

Each data zone is configured with its own local computational framework instance and local storage, allowing each location to operate independently with its own data processing capabilities. This local autonomy eliminates the need for complex shared file system configuration while maintaining distributed processing efficiency.

The patent segments data access authority by maintaining separate data zones for different entities, with each zone's computational framework operating independently on its own data. This segmentation ensures that data remains within organizational boundaries while still enabling distributed collaborative analytics through framework coordination.

The computational framework acts as an intermediary that enables data processing without direct data sharing. The framework coordinates computation across data zones and aggregates results, allowing collaborative analytics while keeping raw data confined to its original ownership boundaries and eliminating privacy concerns associated with data copying.