Extensible Data Model for Contact Center Integration
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Solution Overview
Problem
Current contact center software systems face challenges in integrating enterprise database applications without causing semantic inconsistencies or reporting inaccuracies, particularly due to difficulties in managing type-2 slowly changing dimensions and ensuring proper logical semantics across different databases.
Innovation Solution
The implementation of an extensible data model that includes a first and second database managed by different parties, with a work queue for data exchange and an ETL module for data transformation, allowing unprivileged parties to extend the database model while protecting the proprietary schema and ensuring semantic compatibility, using external tables and triggers to enforce type-2 dimension semantics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional data warehouse generates and assigns surrogate keys to identify entities represented on multiple data sources, then data correlation across sources can be achieved, but integration of enterprise database applications with contact center software leads to semantical inconsistencies and reporting inaccuracies
Solution Approach 1:
The patent introduces an intermediary layer (the extensible data model with work queues and ETL modules) between enterprise database applications and contact center software. This intermediary handles data transformation and correlation, allowing both systems to maintain their own semantic models while achieving accurate data integration through the mediating structure.
Solution Approach 2:
The patent segments the integrated system into distinct components: enterprise databases, contact center databases, work queues, and ETL modules. Each component maintains its own data model and semantics, with clear boundaries and interfaces. This segmentation allows independent development and maintenance while achieving integration through standardized interfaces.
2Reliability
If database triggers are used to watch for third party changes and apply type-2 semantics, then proper logical semantics can be maintained, but race conditions between the application and third party prevent proper semantics from being maintained
Solution Approach 1:
The patent implements preliminary action by having the ETL module proactively monitor for changes in enterprise databases and prepare data transformations before conflicts occur. Work queues are established in advance to capture and buffer data changes, ensuring that type-2 semantics are applied systematically before race conditions can arise.
Solution Approach 2:
The work queue acts as an intermediary buffer between third-party database changes and the contact center database updates. This intermediary decouples the timing of data generation from data application, allowing type-2 semantics to be applied consistently without race conditions between simultaneous operations.
3Ease of manufacture
If the contact center software vendor desires ease of integration with existing enterprise database application software, then integration complexity is reduced, but semantical inconsistencies and software malfunctions result from conflicts
Solution Approach 1:
The patent enables parameter changes by allowing the extensible data model to be customized through specifications that define new tables, columns, and relationships. This customization capability allows the system to adapt to different enterprise database schemas and semantic models without requiring complex integration logic, while maintaining data integrity through controlled modification processes.
4Adaptability or versatility
If unprivileged parties are allowed to extend the database model, then adaptability and customization are improved, but protection of proprietary schema and prevention of semantic conflicts become difficult
Solution Approach 1:
The patent segments database access and modification privileges by introducing work queues that unprivileged parties can populate with data extension specifications. The actual database schema modifications are performed by privileged ETL modules that process these specifications. This segmentation allows unprivileged parties to propose extensions while maintaining schema integrity through controlled processing.
Solution Approach 2:
The system enables self-service data extension through a declarative specification mechanism. Unprivileged parties can define their own data extensions by submitting specifications to work queues without needing direct database access or understanding of complex schema constraints. The ETL module automatically processes these specifications and applies appropriate semantic rules, allowing self-service while maintaining integrity.
Data Source
AI summary
In one embodiment, a data processing system includes a first database 304 maintained by a first party (such as a customer); a second database 324, 328 maintained by a second party (such as a vendor) different from the first party; a work queue 316 to specify data to be added to the second database; an extract, transform and load module 308 to write data from the first database to the work queue; and a data import module operable 320 to import data from the work queue to the second database. In the system, the first party is not privileged to write the data directly to the second database but is privileged to write the data to the work queue. The import module transforms the data written to the work queue to a form semantically compatible with the second database.