Dataset Sortedness Evaluation for Data Processing Flow Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing systems incur significant overhead and processing time due to the need for pre-sorting datasets, even if the data is already sorted, which can result in wasted cycles and increased memory usage.
Innovation Solution
A method and system that evaluate the 'sortedness' of a dataset subset, allowing the system to skip unnecessary pre-processing sorting steps if the data is found to be sorted, and perform sorting only if the data is unsorted, using a sampling technique to determine the sortedness with a pre-defined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-sorting is always performed on datasets before processing, then data processing correctness is ensured, but processing time and computational overhead increase significantly
Solution Approach 1:
The patent applies preliminary action by performing a quick evaluation of the subset of data to determine if sorting is needed before the main processing operation. This preliminary check allows the system to prepare in advance (determine sortedness) and only perform the full sorting operation if absolutely necessary, thus resolving the contradiction between ensuring correctness and minimizing time loss.
Solution Approach 2:
The patent uses partial action by evaluating only a subset of the data rather than the entire dataset to determine if sorting is needed. This partial evaluation is sufficient to make the determination in most cases, significantly reducing the time overhead while maintaining reliability - if the subset is sorted, the entire dataset is likely sorted and no full sort is needed.
2Reliability
If pre-sorting is always performed on datasets, then correct operation of processing algorithms is ensured, but memory usage and computational overhead increase
Solution Approach 1:
The system performs a preliminary evaluation of data sortedness using a subset before committing to the energy-intensive full sorting operation. This preliminary check ensures algorithm correctness is maintained while avoiding unnecessary computational overhead and energy consumption associated with always performing full pre-sorting.
Solution Approach 2:
By performing partial evaluation on a subset rather than the complete dataset, the system dramatically reduces computational overhead and energy usage while still providing sufficient information to determine whether full sorting is necessary for algorithm correctness.
3Measurement precision
If the entire dataset is evaluated for sortedness, then accurate determination is achieved, but processing time increases
Solution Approach 1:
The patent applies partial action by evaluating only a subset of the dataset to determine sortedness. This subset evaluation provides sufficiently accurate measurement in the vast majority of cases while dramatically reducing the time required compared to evaluating the entire dataset. The subset size is chosen to balance accuracy and speed.
Solution Approach 2:
The evaluation mechanism is self-service in that it uses the data itself to determine whether sorting is needed. By sampling the subset and checking its sortedness, the system automatically makes the determination without requiring external intervention or full dataset processing, thus achieving good accuracy with minimal time loss.
Data Source
AI summary
One or more processors evaluate whether a subset of a dataset is sorted. One or more processors process the dataset if it is determined that the subset of the dataset is sorted.