Central Data Architecture for Real-Time Experience Event Processing
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Solution Overview
Problem
Existing systems for processing digital experience information lack real-time data processing capabilities, are cost-prohibitive for handling large data volumes, and require complex integration and intricate data schema designs, making them cumbersome to use.
Innovation Solution
A computer-implemented system with a central data location that includes an ingestion connector, translator, event backbone, and event store, capable of transforming transactional event data into a common data scheme, allowing real-time streaming and storage, and utilizing business rules and connectors for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing systems of record are used to collect and store digital experience information centrally, then data storage and retrieval are simplified, but real-time data processing capability is lost
Solution Approach 1:
The system segments data processing into two distinct pathways: a batch processing pathway for historical data storage and retrieval, and a streaming processing pathway for real-time data analysis. This segmentation allows the system to maintain centralized data collection benefits while enabling real-time processing capabilities through separate architectural components.
Solution Approach 2:
The patent introduces a streaming processor as an intermediary component between the centralized data store and analysis systems. This mediator receives data from the central location and provides real-time streaming capabilities, effectively bridging the gap between batch-oriented storage and real-time processing requirements.
2Quantity of substance
If existing systems handle large volumes of digital experience data, then comprehensive data collection is achieved, but processing costs become prohibitive
Solution Approach 1:
The system extracts only the necessary data elements for real-time processing from the large volume of available data, rather than processing all data centrally. By taking out specific high-value data points for streaming analysis while leaving bulk data in cost-effective storage, the system reduces processing costs while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The patent implements partial processing by applying real-time streaming analysis to only a subset of data that requires immediate attention, while the remainder of data is processed through more cost-effective batch methods. This selective approach reduces overall processing costs while ensuring critical data receives timely analysis.
3Productivity
If complex integration and intricate data schema designs are used in existing systems, then data processing capability is achieved, but system usability becomes cumbersome
Solution Approach 1:
The patent implements a universal data schema that can handle multiple data types and sources through a common structure. This multi-functional schema design eliminates the need for complex, specialized integration logic for each data source, thereby maintaining robust data processing capability while significantly improving system usability and ease of operation.
Solution Approach 2:
The system uses template-based data schemas that can be copied and adapted for different data sources rather than designing unique integration logic for each. This copying approach maintains comprehensive data processing capabilities across diverse sources while reducing the complexity and improving usability of system implementation.
Data Source
AI summary
In one implementation, systems and methods are provided for processing digital experience information. A computer-implemented system for processing digital experience information may comprise a central data location. The central data location may comprise a connector that may be configured to receive information belonging to a category from an information source; an event backbone that may be configured to route the information received by the connector based on the category; a translator that may be configured to transform the received information into a common data model; and a database that may be configured to store the received information. The event backbone may be further configured to send information to the connector from the event backbone and the database based on one or more criteria.