Data Integration Layer for Heterogeneous Enterprise Systems
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Solution Overview
Problem
Business enterprises face challenges in integrating data across disparate databases with different data models, semantics, and syntax, which complicates customer service and e-commerce operations by requiring redundant data entry from customers and service representatives.
Innovation Solution
A system and method that identify common data elements with equivalent semantics across heterogeneous data sources, harmonize their syntax, and create a unified database, allowing access to integrated data for users and customer service representatives, thereby pre-filling web documents with relevant customer information from multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in disparate databases with different data models and semantics, then each system can maintain its own data structure and semantics, but data integration becomes difficult and requires redundant data entry
Solution Approach 1:
The patent introduces a data integration layer that acts as an intermediary between disparate databases. This layer includes components such as a data dictionary, mapping rules, and transformation logic that enable data from different sources with different semantics to be integrated without changing the underlying systems. The intermediary translates and harmonizes data from multiple sources into a unified view.
Solution Approach 2:
The patent segments the data integration problem into multiple independent components: data extraction from source systems, data transformation and mapping, data loading into target systems, and data synchronization. Each component can be developed, maintained, and modified independently, reducing overall system complexity while enabling integration.
2Adaptability or versatility
If data is stored in disparate databases with different syntax, then each system can use its own data format, but data comparison and matching become difficult
Solution Approach 1:
The patent applies parameter changes by transforming data from different syntax formats into a standardized format. The system includes syntax mapping rules that convert data elements from various source systems into a common syntax structure, enabling accurate comparison and matching while preserving the original syntax flexibility of source systems.
Solution Approach 2:
A syntax translation layer serves as an intermediary that receives data in various syntax formats from different sources, applies transformation rules, and outputs data in a standardized syntax. This intermediary enables accurate data matching without requiring changes to the source systems' syntax preferences.
3Adaptability or versatility
If data elements have different semantics across systems, then each system can define its own meaning for data elements, but data integration requires complex mapping and validation
Solution Approach 1:
The patent creates a universal data model that can represent multiple semantic meanings for the same data element. The system includes a semantic mapping framework that allows a single data structure to accommodate different interpretations from various source systems, enabling one integration solution to handle multiple semantic variations efficiently.
Solution Approach 2:
A semantic mapping layer acts as an intermediary that translates data elements with different semantics into a unified semantic model. This layer includes mapping rules that define relationships between source system data elements and target system data elements, enabling efficient integration while preserving semantic independence of source systems.
4Ease of operation
If customers must enter the same information multiple times across different systems, then each system can have its own data collection process, but customer service productivity decreases
Solution Approach 1:
The patent merges data collection processes across multiple systems by implementing a unified data entry interface. When a customer enters information once, the system automatically distributes and integrates this data across all connected systems through the data integration layer, eliminating redundant entry while maintaining system autonomy for data processing.
Solution Approach 2:
A data distribution intermediary receives data from a single source and automatically routes it to multiple target systems. This intermediary includes logic for data validation, transformation, and distribution, enabling customers to enter information once while ensuring all systems receive the necessary data in their required formats.
Data Source
AI summary
A method and system for integrating data across different systems is disclosed. Data in a plurality of databases are integrated by identifying the common data elements with equivalent semantics and selecting a preferred syntax for the data. A new record including the common data elements and data with preferred syntax is made available to users.