Cognitive Rule Engine for Data Analysis Precedent Identification
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Solution Overview
Problem
Existing data management and analysis systems lack efficient methods for identifying common data analysis techniques across sessions and providing these as precedents to improve data analysis workflows.
Innovation Solution
The system monitors data analysis sessions, determines common data analysis techniques, and provides these techniques as precedents to a precedent engine, enabling improved data analysis by leveraging established methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data analysis sessions are monitored and common techniques are identified, then data analysis efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary monitoring of data analysis sessions to identify common techniques before they are needed. By capturing and storing precedent techniques in advance, the system enables faster data analysis workflows without increasing operational complexity during actual analysis tasks.
Solution Approach 2:
The system creates copies of successful data analysis techniques as precedents that can be reused. Instead of requiring complex real-time analysis, the system copies and applies previously successful techniques, reducing the cognitive load and time required for new analysis tasks while maintaining efficiency.
2Manufacturing precision
If precedents are provided to improve workflow consistency, then data analysis quality is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary identification and storage of common data analysis techniques as precedents. This advance preparation allows the system to quickly retrieve and apply appropriate techniques during actual data analysis, improving workflow consistency without significant time penalty since the heavy lifting of technique identification occurs beforehand.
Solution Approach 2:
The system incorporates feedback loops that monitor data analysis sessions and refine precedent identification. By continuously learning from actual usage patterns, the system improves the accuracy of precedent matching over time, reducing the time needed for technique selection while maintaining high workflow consistency.
Data Source
AI summary
In an aspect, provided is a method comprising monitoring one or more data analysis sessions, determining, based on the monitoring, a common data analysis technique performed across common data analysis sessions, identifying the common data analysis technique as a precedent, and providing the precedent to a precedent engine.