Smart Data Retention Insight Expansion via Knowledge Graph Mapping
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Solution Overview
Problem
As data storage capacity and technology increase, the amount of data collected and stored grows, leading to increased computing power and time needed for analysis, which can be inefficient and resource-intensive.
Innovation Solution
A computer-implemented method and system that identifies common data sources, analyzes their schemas, maps features across domains, generates a knowledge graph, and uses a sync-adapter and reverse insight adapter to apply insights from one data source to multiple sources, reducing computing usage by performing detailed analysis on a small portion of data and leveraging insights across all sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data storage capacity and technology increase to collect and store more data, then the amount of data available for analysis increases, but the computing power and time needed for analysis increases
Solution Approach 1:
The patent segments the data analysis process into two distinct phases: (1) detailed analysis of a small representative subset of data sources to extract insights, and (2) application of these insights across all remaining data sources. This segmentation allows the system to avoid performing computationally intensive analysis on every single data source, thereby reducing overall computing power and time requirements while still achieving comprehensive data utilization.
Solution Approach 2:
The patent performs preliminary analysis on a small subset of data sources first to extract insights and patterns. These insights are then applied to the remaining data sources without requiring the same level of detailed analysis. This preliminary action approach allows the system to establish a foundation of knowledge that can be leveraged across all data, reducing the computational burden that would otherwise be required to analyze every data source in detail.
2Loss of information
If detailed analysis is performed on all data sources, then comprehensive insights are obtained, but computing resources are consumed inefficiently
Solution Approach 1:
The patent creates a copy of the insights and analysis results obtained from the small subset of data sources, and then applies this copied knowledge to all other data sources. Instead of performing unique detailed analysis on each data source, the system copies the analytical framework and insights gained from the representative subset and applies them across the entire dataset, maintaining comprehensive insight generation while dramatically improving computing resource efficiency.
3Use of energy by moving object
If insights are applied from one data source to multiple sources, then computing usage is reduced, but the complexity of mapping and linking schemas increases
Solution Approach 1:
The patent creates a universal schema mapping framework that enables insights to be applied across multiple data sources with different schemas. By establishing a common domain schema and creating mappings between various data source schemas and this universal schema, the system achieves multi-functionality where a single insight generation process can be applied to diverse data sources. This universal approach, while requiring initial mapping complexity, ultimately reduces overall computing usage by avoiding redundant analysis across different data sources.
Data Source
AI summary
A computer-implemented method applies insights from a variety of data sources to each of the data sources. The method includes identifying a set of data sources, wherein each of the data sources are associated with a domain. The method includes analyzing documentation for each of the data sources. The method further includes extracting a set of attributes for each data source, and determining a data schema associated with each data source. The method includes mapping each data schema to a common domain schema. The method also includes linking, based on the mapping and on the set of attributes for each data source, common features across each data source. The method includes generating, in response to the linking, a knowledge graph. The method further includes preparing a visual display for a set of domain insights; and forking the set of domain insights into a first data source.