Predictive Database for Computer Process Input
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Solution Overview
Problem
Users often face challenges in entering accurate and complete information for computer processes, leading to failures or unintended outcomes, especially as the number of required items increases, resulting in a lower success rate for these processes.
Innovation Solution
A predictive database system that analyzes historic input data from successful computer processes to generate decision trees and rules, which are then filtered and stored to assist in determining necessary user-specific input data, reducing the amount of information users need to enter and improving process success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more items of information are required for computer processes, then the completeness of input data may improve, but the likelihood of user error and process failure increases
Solution Approach 1:
The system automatically determines and supplies additional input data items that are required for successful computer processes, without requiring users to manually identify or enter them. The predictive database autonomously analyzes historic data patterns and generates missing input items based on learned relationships, allowing the system to serve itself in completing the input data.
Solution Approach 2:
The predictive database pre-processes historic input data during an offline training phase to identify patterns, relationships, and optimal input combinations before actual computer processes are executed. This preliminary analysis enables the system to quickly suggest or auto-fill required input items during runtime, reducing user burden while maintaining completeness.
2Measurement precision
If users manually enter all required information, then data accuracy may be maintained, but time consumption and user burden increase
Solution Approach 1:
Users are only required to enter partial input data - specifically, the most critical or easily identifiable items. The predictive database automatically determines and supplies the remaining required input items based on learned patterns from historic data, reducing user effort while maintaining overall data accuracy through the system's predictive capabilities.
3Ease of operation
If a predictive database is implemented to auto-fill data, then user input requirements decrease, but system complexity increases
Solution Approach 1:
The predictive database acts as an intermediary layer between the user interface and the computer process execution system. It receives partial user input, automatically determines missing required items using historic data patterns, and supplies the complete set of input data to the computer process, thereby shielding users from system complexity while maintaining ease of operation.
4Measurement precision
If extensive historic data is analyzed to improve predictions, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The predictive database architecture segments processing into two distinct phases: an offline training phase where extensive historic data is analyzed to build predictive models and identify patterns, and an online inference phase where pre-learned patterns are quickly applied to determine missing input items. This segmentation allows comprehensive data analysis without impacting real-time performance, as the heavy computational work is performed beforehand.
Data Source
AI summary
Systems, methods, and computer program products for implementing a predictive database that improves the success rate of computer processes. Historic input data including user-entered data objects for each of several computer processes is received. Several relationships are determined based on the historic input data, where each of the relationships includes a data object entered for one of the success computer processes linked to another data object entered for the successful computer process. A decision tree is generated and filtered based on the relationships and an optimization threshold. Several rules are generated based on the filtered decision tree and are stored in the predictive database. Thereafter, in response to receiving partial input data entered by a user in relation to a requested computer process, additional input data is determined and supplied to the requested computer process based on the rules and the partial input data.