Directed Graph Workflow for Platform-Agnostic Data Classification
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Solution Overview
Problem
Current data classification systems in social networking systems require significant computational resources and manual configuration, making them inefficient for repeated data classification experiments, especially when dealing with large and complex data sets.
Innovation Solution
A classification platform system that utilizes a graphical user interface to define and manage classification experiments through directed graphs, allowing for the graphical placement and connection of transformation blocks, enabling the reuse of pre-computed results and reducing unnecessary computation via memoization, and allowing for dynamic modification of the graph during execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration is used for each data classification experiment, then flexibility and control are improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent implements preliminary action by pre-configuring transformation blocks and their connections in a directed graph before actual data classification execution. The workflow is designed and validated in advance, allowing repeated experiments to simply reuse the pre-established configuration rather than manually reconfiguring each time. This includes pre-defining data extraction rules, transformation logic, and classification parameters.
Solution Approach 2:
The patent applies copying by creating a reusable directed graph representation of the classification workflow that can be instantiated multiple times with different input data. Once the transformation blocks and their relationships are defined in the graph, this configuration can be copied and executed repeatedly without manual reconfiguration, significantly reducing the time for repeated experiments while maintaining full control over the classification logic.
2Reliability
If complete data processing is performed for each experiment, then accuracy is maintained, but computational resources are wasted on repeated transformations
Solution Approach 1:
The patent segments the data classification process into distinct transformation blocks within a directed graph, where each block performs a specific transformation or analysis function. This segmentation allows the system to identify and cache intermediate results from individual blocks, enabling selective re-execution only when necessary to maintain accuracy while avoiding redundant computation across the entire workflow.
Solution Approach 2:
The system performs preliminary computation of transformation blocks and caches their results before actual classification execution. When the same transformation is needed again, the cached results are reused, maintaining classification accuracy while dramatically reducing computational resource consumption. This is particularly effective for expensive operations like feature extraction or data cleaning that don't change between experiments.
3Device complexity
If static workflows are used, then system simplicity is maintained, but adaptability to different data types and experiments is reduced
Solution Approach 1:
The patent implements dynamics by allowing the directed graph workflow to be dynamically modified during execution. Transformation blocks can be added, removed, or reconfigured based on the specific experiment requirements and data characteristics. The graph structure supports runtime adjustments while maintaining the overall systematic framework, providing both simplicity and adaptability.
Solution Approach 2:
The directed graph workflow system is designed with universality, where transformation blocks serve multiple functions across different data classification experiments. A single transformation block can handle various data types and transformation operations, and the graph structure itself is platform-agnostic, allowing the same workflow to be applied to different computation platforms and experiment types without requiring complete redesign.
4Productivity
If platform-specific implementations are used, then optimization for specific hardware is achieved, but portability and reusability across different computation platforms are lost
Solution Approach 1:
The patent achieves platform independence through a computation platform-agnostic directed graph representation. The workflow defines transformation logic and data flow in a platform-neutral manner, allowing the same classification experiment to be executed on different computation platforms (e.g., distributed systems, cloud platforms, local servers) without modification. This universality enables portability while maintaining the ability to optimize for specific platforms when needed.
Solution Approach 2:
The directed graph serves as an intermediary layer between the classification logic and the underlying computation platform. This intermediate representation allows the workflow to be defined once and then translated or adapted to different platform-specific implementations, achieving both portability and platform optimization. The graph structure mediates between the universal classification requirements and platform-specific execution capabilities.
Data Source
AI summary
Various embodiments include a classification platform system. A user can define a classification experiment on the classification platform system. For example, the user can define an input data space by selecting at least one of data sources interfaced with the classification platform system and defining a workflow configuration including a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow. The DG can specify how one or more outputs of each of the transformation blocks are fed into one or more other transformation blocks. The DG can be executed by various types of computation platforms. The classification platform system can schedule the experiment workflow to be executed on a distributed computation platform according to the input data space and the workflow configuration.