Knowledge Graph Programming Framework for Real-Time Enterprise Data
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Solution Overview
Problem
Business enterprises face challenges in obtaining timely and relevant data analytics, as traditional reporting methods often take several months to deliver specialized reports, making the data outdated by the time it is received.
Innovation Solution
A system that utilizes a knowledge graph to represent enterprise data, incorporating it with publicly accessible data and analytics, allowing for real-time data processing and querying through a search input engine that invokes relevant applications to provide immediate and contextually relevant results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional reporting methods are used to provide specialized reports, then the reports can be comprehensive and detailed, but the turnaround time becomes excessively long (three to six months)
Solution Approach 1:
The system performs preliminary actions by continuously maintaining a knowledge graph that pre-processes and indexes enterprise data structures, relationships, and metadata. This pre-prepared knowledge graph enables rapid query processing without requiring time-consuming data processing during report generation, thus reducing turnaround time while maintaining comprehensive and relevant reports.
Solution Approach 2:
The knowledge graph serves as an intermediary between raw enterprise data and user report requests. It mediates by transforming complex data queries into efficient graph traversals, enabling fast retrieval of relevant information without directly processing raw data for each report request, thereby significantly reducing turnaround time.
2Measurement precision
If comprehensive data processing is performed for specialized reports, then the data analysis can be thorough, but the processing time increases significantly
Solution Approach 1:
The system performs preliminary data processing by continuously building and updating the knowledge graph with structured representations of enterprise data, including relationships and metadata. This pre-processing eliminates the need for time-consuming data transformation and processing during report generation, enabling both thorough analysis and fast delivery.
Solution Approach 2:
The patent replaces traditional mechanical data processing systems with a knowledge graph-based system that uses graph theory and traversal algorithms. This substitution enables efficient querying of complex relationships without performing exhaustive data processing, thereby maintaining analytical quality while dramatically improving generation speed.
3Reliability
If data is processed and reported through traditional IT groups, then the data can be structured and formatted properly, but the delivery time becomes outdated before use
Solution Approach 1:
The system enables self-service data access where users can directly query the knowledge graph without relying on IT groups for data processing and delivery. The knowledge graph maintains current data structures and relationships automatically, allowing users to access fresh, structured data on-demand without waiting for periodic report cycles that cause data to become outdated.
Solution Approach 2:
The knowledge graph is continuously updated and maintained with current enterprise data, ensuring data freshness is preserved. This continuous maintenance eliminates the periodic nature of traditional reporting, allowing users to access the most current structured data at any time without waiting for scheduled report cycles.
Data Source
AI summary
An application comprises program code that includes API tags, which during execution of the application may be resolved to reference-able data objects. The data objects may be objects in a knowledge base. The API tags decouple the program code from the specific data contained in the knowledge base, allowing for applications that access the knowledge base to be written independently of the knowledge base; even before the knowledge base is loaded with data.