Data Processing Method for Time-Optimized Financial Statement Calculation
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Solution Overview
Problem
Existing data processing methods for mainframe computer systems face challenges in ensuring timely and accurate financial statements at the end of a period, as end of day processing and end of month/quarter/year processing are interdependent, leading to potential inaccuracies and increased computational load due to large data volumes.
Innovation Solution
A data processing method that determines a time span for calculation, assesses the probability of data changes, prioritizes calculations based on change probability, recalculates as necessary, and stores results to ensure accuracy and efficiency by anticipating and correcting result data sets before the end of the period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end of month/quarter/year processing is performed after end of day processing is completed, then accuracy of financial statements is improved, but time required for processing increases
Solution Approach 1:
The patent applies preliminary action by calculating result data sets in advance during the time span before the period ends. The system determines a time span for calculation and performs calculations before the end of the period, so that when the period ends, the financial statements are ready. This allows end of month/quarter/year processing to start before end of day processing is fully completed, reducing the time required while maintaining accuracy through subsequent verification and correction steps.
2Productivity
If all result data sets are calculated simultaneously at the end of the period, then computational resources are optimized, but data changes may render calculations inaccurate
Solution Approach 1:
The system performs preliminary calculations of result data sets during a determined time span before the period ends. By calculating in advance rather than simultaneously at the end, the system can optimize computational resources while maintaining the ability to correct for data changes. The method determines which result data sets to calculate first based on the time span and period end date, allowing staged computation.
Solution Approach 2:
The patent implements feedback by verifying calculated result data sets against actual data changes after the calculation time span. The system determines whether data changes occurred during the time span and, if so, performs corrections to the calculated result data sets. This feedback mechanism ensures accuracy while allowing efficient batch processing during the time span.
3Reliability
If data processing is performed closer to the end of the period, then accuracy is improved by including all transactions, but computational load increases
Solution Approach 1:
The patent applies preliminary action by determining a time span for calculation that ends before the period end date. The system calculates result data sets during this predetermined time span, which allows computational load to be distributed over a longer period rather than concentrated at the end. This reduces peak computational demand while still ensuring all transactions up to the period end are included through subsequent verification and correction steps.
Data Source
AI summary
In one example, a data processing method is provided for the time-optimized calculation of a large number of result data sets at the end of a period. The calculation can be based on data. A time span can be determined for which the underlying data are to be taken into consideration. For each result data set, the probability that the data underlying the calculation of the respective result data set are changed between the calculation of the result data set and the end of the period can be detected. Result data sets within the time span can be calculated. Those result data sets of which the data underlying the calculation have a minor probability of being changed can be calculated prior to those result data sets of which the data underlying the calculation have a higher probability of being changed. The data sets can be stored.