Data Processing Pipeline With Context Transfer for Dynamic Configuration
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Solution Overview
Problem
Existing data pipelines primarily transfer data without context, leading to inefficiencies in dynamic configuration and interdependency between processing elements, particularly in complex tasks like machine learning, where parameter values are often defined upfront and cannot adapt to real-time conditions.
Innovation Solution
A data processing pipeline that transfers both data and context between processing elements, allowing elements to dynamically configure their operations based on received context, enabling dynamic parameter adjustments and enhancing interdependency through context-driven parameter exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If processing elements transfer only data without context, then the pipeline structure remains simple, but the adaptability and dynamic configuration capability of processing elements deteriorates
Solution Approach 1:
The patent merges data transfer with context transfer into a unified communication mechanism. Processing elements transmit both data and contextual information (parameters, configuration settings, state) through the same pipeline infrastructure, enabling dynamic configuration without adding separate communication channels or complex structural elements.
Solution Approach 2:
Processing elements use the received context information to automatically configure their own operations and parameters. Each processing element dynamically adjusts its behavior based on the context received from upstream elements, eliminating the need for external configuration mechanisms or complex control logic.
2Adaptability or versatility
If parameter values are defined upfront in traditional pipelines, then the pipeline implementation is straightforward, but the ability to adapt to real-time conditions deteriorates
Solution Approach 1:
The patent transforms static parameter definitions into dynamic parameter adjustment. Parameters are no longer fixed at pipeline creation time but are continuously updated based on real-time context information flowing through the pipeline, allowing the system to adapt to changing conditions while maintaining a straightforward implementation approach.
Solution Approach 2:
The patent enables parameter values to change dynamically during pipeline execution based on incoming context information. Processing elements receive context containing parameter specifications and automatically adjust their operational parameters in real-time, facilitating adaptation to real-time conditions without complex reconfiguration mechanisms.
3Productivity
If context is transferred between processing elements, then dynamic parameter adjustments are enabled, but the amount of data transferred and processing overhead increases
Solution Approach 1:
The context information structure is designed to serve multiple purposes simultaneously: it configures processing parameters, conveys operational state, and provides control information. This multi-functionality reduces the need for separate data structures and minimizes overall data transfer volume while enabling comprehensive dynamic control.
Data Source
AI summary
An example operation may include one or more of executing a first processing element among a sequence of processing elements within a data processing pipeline on input data to generate a first output, transferring the first output and context associated with the first processing element from the first processing element to a second processing element among the sequence of processing elements, within the data processing pipeline, executing the second processing element on the first output and the context to generate a second output, and storing the second output in memory.