Database Revalidation via Parallel Distance-Based Groups
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Solution Overview
Problem
Large databases with massive data sets face significant time constraints when revalidating dependent data objects due to complex interdependencies, making existing sequential revalidation processes inefficient.
Innovation Solution
Implementing a parallelized revalidation process that groups data objects by their maximum dependency distance, allowing for parallel execution while maintaining dependency order to avoid redundant validation and optimize processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential revalidation process is used, then dependency order is maintained, but revalidation time increases significantly
Solution Approach 1:
The patent segments the revalidation process by grouping dependent data objects into multiple batches based on their dependency distance from the root object. Objects at the same dependency distance are grouped together and can be validated in parallel, while maintaining the overall dependency order across batches. This segmentation enables parallel processing without compromising the reliability of dependency validation.
2Loss of time
If parallel processing is implemented, then revalidation time decreases, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the dependency distance for each data object from the root object before the revalidation process begins. This pre-computed information is used to automatically group objects into parallel execution batches, eliminating the need for complex real-time dependency analysis during parallel processing. The complexity is shifted to a one-time setup phase, simplifying the actual parallel execution.
3Reliability
If all dependent objects are revalidated, then validation completeness is ensured, but processing time increases
Solution Approach 1:
The patent implements partial action by revalidating data objects in structured batches based on dependency distance rather than validating all objects individually in sequence. Objects at the same dependency level are validated together in parallel, providing sufficient validation coverage while dramatically improving processing efficiency. This approach validates all necessary objects without the overhead of strict sequential processing.
Data Source
AI summary
Database revalidation may be provided by parallelized execution of distance-based groups. A change to a first data object of the plurality of data objects is received. The first data object has a plurality of dependent data objects in the plurality of data objects. A maximum dependency distance for the respective dependent data objects in the plurality of dependent data objects is calculated. The respective dependent data objects in the plurality of dependent data objects are grouped into one or more groups based on their respective maximum dependency distance. A given group of the one or more groups represents a given maximum dependency distance. The one or more groups are executed on in order from smallest to largest of their respective representations of maximum dependency distance. Executing on the one or more groups includes revalidating the data objects of a respective group in parallel.