Statistical Array Parallel Data Processing
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Solution Overview
Problem
Current data processing methods for big data statistics are time-consuming and laborious, requiring traversal of sample data for each item to obtain statistical results, leading to performance issues and high redundancy in computational resources.
Innovation Solution
A data processing method that uses a statistical array to perform calculations on multiple indicator values in parallel, allowing for efficient statistics generation and reducing the need for frequent updates, thereby improving computational efficiency and reusability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used to perform statistics on big data, then statistical results can be obtained, but the processing time is long and computational resources are excessive
Solution Approach 1:
The patent segments the statistical processing task by dividing sample data into multiple statistical groups and using multiple statistical arrays to process different groups in parallel. Each statistical array independently processes a specific statistical indicator, enabling simultaneous computation across multiple data segments without sequential traversal, thus dramatically reducing processing time while maintaining accuracy.
2Measurement precision
If traditional sequential traversal methods are used for statistical analysis, then accurate results are obtained, but computational resource redundancy increases
Solution Approach 1:
The patent merges multiple statistical operations into a single parallel processing framework. Instead of sequentially traversing data for each statistical indicator, the system combines multiple statistical arrays that simultaneously process different indicators on the same data segments. This merging approach maintains measurement precision through independent array operations while eliminating redundant computational resources by avoiding repeated sequential traversals.
3Adaptability or versatility
If multiple statistical operations are performed sequentially on the same data, then comprehensive statistical results are achieved, but the complexity of data processing increases
Solution Approach 1:
The patent creates a universal statistical processing framework where multiple statistical arrays can simultaneously handle different statistical indicators on the same data segments. Each array is designed with multi-functionality to process various types of statistical operations (counting, summing, averaging) on different indicators without requiring separate processing logic. This universality enables comprehensive statistical analysis while reducing processing complexity through standardized parallel operations.
Data Source
AI summary
A data processing method, a data processing apparatus, an electronic device, and a computer-readable storage medium are provided. The data processing method includes: acquiring at least one piece of sample data in a statistical group, each piece of sample data including a statistical indicator and an indicator value of the statistical indicator; creating a first statistical array corresponding to the statistical indicator, the first statistical array including a plurality of first elements, and the plurality of first elements being used to perform statistics on different indicator values, respectively; and traversing indicator values of the at least one piece of sample data, and performing statistics on the at least one piece of sample data by using the first statistical array, so as to obtain a data statistical result, the plurality of first elements of the first statistical array being statistical sub-results of respective indicator values, respectively.