Workflow Composition Platform Using Pluggable Functional Blocks
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Solution Overview
Problem
Current computing systems require explicit programming and coding to deliver complex functionalities, which can be inefficient, especially in data computation tasks that benefit from machine learning algorithms.
Innovation Solution
A platform that allows users to compose and execute workflows using pre-built, pluggable functional building blocks without writing code, enabling the creation of complex data transformations and machine learning workflows through a drag-and-connect paradigm, with automatic conversion between different interfaces and data formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If explicit programming and coding are used to deliver complex functionalities, then the system can execute precise computations, but the ease of operation deteriorates and development time increases
Solution Approach 1:
The system segments complex computational functionalities into discrete, pre-built functional building blocks (e.g., data transformation, machine learning, aggregation). Users compose workflows by selecting and connecting these segmented blocks rather than writing code, reducing operational complexity while maintaining computational precision.
Solution Approach 2:
The platform introduces an intermediary layer between the user and the execution environment. This intermediary provides standardized interfaces and automatic conversion mechanisms that translate user-selected building blocks into executable computations, eliminating the need for users to directly interact with complex programming languages or execution details.
2Ease of operation
If pre-built functional building blocks are used without coding, then ease of operation improves, but manufacturing precision (computational accuracy) may deteriorate
Solution Approach 1:
The functional building blocks are designed with universal interfaces that support multiple data types and formats. Each block provides standardized input and output contracts that ensure computational accuracy across different contexts, while the same block can be reused across various workflows without requiring code modifications.
Solution Approach 2:
The system allows dynamic configuration of building block parameters (e.g., data types, formats, transformation parameters) without changing the core functionality. Users can adjust parameters to match specific computational requirements while maintaining the simplicity of the block-based interface, thus preserving both ease of operation and computational precision.
3Ease of operation
If workflows are composed using drag-and-connect paradigm, then ease of operation improves, but device complexity increases due to the need for standardized interfaces and automatic conversion
Solution Approach 1:
The platform performs preliminary actions by pre-defining standardized interfaces and conversion mechanisms for all functional building blocks. The execution environment automatically handles interface conversion and data format transformation before workflow execution, so the complexity is encapsulated in advance rather than being exposed to users during workflow composition.
4Adaptability or versatility
If automatic conversion between interfaces and data formats is implemented, then adaptability improves, but device complexity increases
Solution Approach 1:
The execution environment acts as an intermediary that automatically handles interface conversion and data format transformation between building blocks. This intermediary layer absorbs the complexity of format conversion, allowing building blocks to maintain simple, standardized interfaces while the system as a whole achieves high adaptability across different data types and formats.
Data Source
AI summary
A platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have well known plug types. The building blocks can be composed build complex compositions. Input and output files are converted to a standard data type so that modules are pluggable.