Database Scenario Modeling via Live Data Subset Copying
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Solution Overview
Problem
Enterprise software systems face challenges in efficiently modeling the impact of changes on complex business processes due to the large size and complexity of production databases, making it difficult to test scenarios without affecting the database's availability or causing errors in financial reporting.
Innovation Solution
A method that involves selecting a subset of live data affected by a scenario, copying it to a faster computing resource for modeling, running the scenario on this data, and displaying results, allowing for quick assessment and approval before implementation on the production database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If periodic processes are run on production databases to manage large amounts of data, then business operations can be managed, but the processes take hours or days to complete and significantly affect values stored within a general ledger
Solution Approach 1:
The patent creates a copy of the production database in a modeling environment, allowing scenarios to be tested on the copy rather than the actual production data. This copying enables faster scenario execution while preserving the integrity of the production database, resolving the contradiction between needing fast scenario modeling and the time-consuming nature of running processes on large production datasets.
Solution Approach 2:
The patent segments the database into production and modeling environments, separating the live operational data from the scenario testing data. This segmentation allows periodic processes to be run on the production database while scenarios are tested independently on the modeling copy, enabling parallel processing and eliminating the time loss associated with running all processes sequentially on production data.
2Reliability
If changes to allocation business rules are implemented to improve financial management, then better decision support can be provided, but the impact on the business bottom line is difficult to predict until changes are actually implemented
Solution Approach 1:
The patent enables preliminary action by allowing scenarios to be defined and tested on the modeling copy before actual implementation on the production database. Users can predict the impact of allocation rule changes by running scenarios on the copied data, seeing the projected effects on the bottom line before committing to the changes in the production environment. This eliminates the uncertainty of predicting impact while maintaining the reliability of financial reporting through controlled, testable changes.
3Measurement precision
If the production database is used to test scenarios, then real-time impact can be observed, but the database availability is affected and errors in financial reporting may occur
Solution Approach 1:
The patent resolves this contradiction by copying the production database to a modeling environment. The copy contains sufficient data to accurately assess scenario impact (maintaining measurement precision) while the production database remains intact and available for normal operations. This copying approach allows scenario testing without affecting production database availability or risking errors in financial reporting.
Solution Approach 2:
The modeling copy acts as an intermediary between scenario testing and production operations. It mediates the relationship by absorbing the testing activities, allowing accurate impact assessment while protecting the production database from availability issues and errors. The intermediary copy can be updated from production data as needed, maintaining measurement precision without compromising production reliability.
Data Source
AI summary
A method of modeling a scenario for use with live data in a production database may include selecting the scenario. The live data can be stored in the production database on a first computing resource, and production scenarios can be stored and executed on the live data using the first computing resource. The method may also include identifying a subset of the live data that are affected by the scenario, copying the subset to a second computing resource to create modeling data, running the scenario on the modeling data using the second computing resource, causing a display device to provide an output comprising a result of the scenario on the modeling data, receiving an input indicating that the scenario is approved, and storing the scenario with the plurality of production scenarios for use on the first computing resource.