Synthetic Context Events for Data Analysis
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Solution Overview
Problem
Current computer processing of large data sets neglects relevant context, failing to account for relationships between data of different granularities and cardinalities, leading to incomplete data analysis.
Innovation Solution
A method and system for generating and maintaining synthetic context events by linking context objects with non-contextual data objects, identifying patterns of context frequency over specified time periods, and optimizing these events by adding additional data matching the same pattern across different time periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer processing is carried out linearly by grouping data that shares granularities and cardinalities, then data processing efficiency is improved, but relevant context and relationships between seemingly unrelated data are neglected
Solution Approach 1:
The patent introduces synthetic context objects as intermediary entities that connect non-contextual data objects. These synthetic context objects serve as mediators that enable the computer to establish relationships between data that would otherwise appear unrelated, allowing context information to be recovered without compromising processing efficiency.
Solution Approach 2:
The patent segments data into contextual and non-contextual components, processing them through different pathways. Non-contextual data objects are processed efficiently in groups, while context objects are separately identified and linked to create synthetic context events, enabling both efficient processing and context preservation.
2Loss of information
If context objects are linked with non-contextual data objects to enable non-linear reasoning, then data analysis completeness is improved, but data structure complexity increases
Solution Approach 1:
The patent creates a universal data structure where synthetic context objects can serve multiple functions: they link different non-contextual data objects, represent contextual relationships, and form the basis for synthetic context events. This multi-functionality reduces the need for separate structures for each type of relationship.
Solution Approach 2:
The patent uses synthetic context objects as simplified copies or representations of complex contextual relationships. Instead of storing entire context frameworks, the system creates synthetic representations that capture essential relationships, reducing structural complexity while preserving meaningful context.
3Measurement precision
If synthetic context events are generated by identifying patterns across specified time periods, then data analysis accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary organization of data into synthetic context objects and groups them by potential patterns before conducting full analysis. This preliminary structuring enables faster identification of contextual relationships during the pattern recognition phase, reducing the time required for accurate analysis.
Solution Approach 2:
The patent varies parameters such as the specified time period and frequency thresholds when generating synthetic context events. By adjusting these parameters, the system can balance between analysis accuracy and processing time, allowing users to optimize based on specific needs.
Data Source
AI summary
A method, computer program product and system for generating and maintaining synthetic context events. The steps include searching a data structure of synthetic context-based objects and associated data for a pattern of context exhibited at a first specified frequency within a first specified time period; combining the synthetic context-based objects and associated data exhibiting the pattern of context exhibited at the first specified frequency within the first specified time period into a synthetic context event; and optimizing and maintaining the synthetic context event by searching the data structure for additional synthetic context-based objects and associated data exhibiting a same pattern of context at a second specified time period different than the first specified time period and adding the additional synthetic context-based objects and associated data to the synthetic context event.