Dynamic Stream Processing via Marshalled Data Objects
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Solution Overview
Problem
Service providers face challenges in integrating data processing methods for slow-moving and real-time data, leading to incomplete analytics that fail to capture the value of combining historical and current data for services like recommendation systems.
Innovation Solution
A system that utilizes static and dynamic data analytics by marshalling computational chains as data objects, allowing them to be processed in both static and dynamic processing mechanisms, enabling the integration of slow-moving and real-time data processing for comprehensive results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate data processing architectures are used for slow-moving and real-time data, then each processing mechanism can be optimized for its specific data type, but the system cannot provide comprehensive analytics that combines historical and current data
Solution Approach 1:
The patent introduces a common data object as an intermediary that bridges static and dynamic processing mechanisms. This data object can be processed by both batch analytics systems and stream processing systems, enabling integration of historical and real-time data without requiring separate optimized architectures for each data type. The common data object serves as the mediator that allows both processing mechanisms to work with the same data structure.
Solution Approach 2:
The patent creates a universal data object that can be processed by multiple processing mechanisms (both static batch and dynamic stream processing). This multi-functional data object eliminates the need for separate data representations for different processing types, allowing the system to provide comprehensive analytics by combining results from both historical and real-time processing approaches.
2Productivity
If traditional isolated processing methods are used for historical and real-time data, then processing simplicity is maintained, but the system fails to capture the value of combining data types for improved services
Solution Approach 1:
The patent merges the processing of historical and real-time data by using a common data object that can be handled by both batch and stream processing mechanisms. This combination enables the system to provide enhanced services such as improved recommendations that consider both historical user behavior and current real-time activity, thereby increasing service value.
Solution Approach 2:
The common data object acts as an intermediary that enables integration between isolated processing systems. By using this mediator, the system can combine historical and real-time data processing results to create comprehensive analytics that drive improved service outcomes.
3Measurement precision
If data processing is segregated by data type, then processing efficiency for each type is maximized, but comprehensive analytics requiring both historical and real-time data becomes unachievable
Solution Approach 1:
The patent creates a universal data object that maintains the benefits of type-specific processing while enabling comprehensive analytics. The data object can be processed by both batch and stream mechanisms, allowing the system to achieve high measurement precision through specialized processing while maintaining adaptability to provide integrated analytics.
Solution Approach 2:
The common data object serves as an intermediary that enables flexible integration of historical and real-time data processing. This mediator allows the system to adapt to different processing requirements while maintaining the precision benefits of specialized processing mechanisms for each data type.
Data Source
AI summary
An approach is provided for integrating various data processing methods for more accurate and comprehensive results. A data processing mechanism determines at least one processing element of at least one dynamic processing mechanism. Further, the data processing mechanism causes a marshalling of the at least one processing element as at least one data object, wherein the at least one data object is processable by at least one static processing mechanism.