Directed Graph Data Classification Workflow
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Solution Overview
Problem
Current data classification systems require significant computational resources and manual reconfiguration for each experiment, making them inefficient for processing large and complex data sets, such as those in social networking systems, where repeated analysis and updates are necessary.
Innovation Solution
A classification platform system that utilizes a directed graph (DG) to define and manage classification experiments, allowing for graphical arrangement of transformation blocks, dynamic modification during execution, and memoization to reduce re-computation, enabling efficient reuse of data features and model updates across experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data classification experiments are performed repeatedly on large and complex data sets, then analysis coverage and insight depth are improved, but computational resource consumption and cycle time increase significantly
Solution Approach 1:
The system performs preliminary actions by caching intermediate computation results (features, model outputs) during the first execution of a classification experiment. When the same experiment is repeated, these pre-computed results are retrieved from cache rather than re-computed, significantly reducing cycle time while maintaining complete analysis coverage.
Solution Approach 2:
The directed graph workflow representation enables universal reuse of computation pipelines across multiple experiments. The same graph structure can be executed with different parameters or data inputs without reconfiguration, allowing the system to handle various classification tasks using a unified framework that reduces overall computational overhead.
2Adaptability or versatility
If manual reconfiguration is performed for each data classification experiment, then experiment flexibility and customization are improved, but operational complexity and time consumption increase
Solution Approach 1:
The system employs dynamic parameter configuration where the workflow structure (directed graph) remains fixed but execution parameters can be dynamically adjusted for each experiment. This allows flexibility in customization without requiring manual reconfiguration of the entire pipeline, reducing operational complexity while maintaining adaptability.
Solution Approach 2:
The directed graph workflow definition serves as a reusable template that can be copied and executed multiple times with different parameters. Instead of manually reconfiguring each experiment from scratch, users can replicate existing workflow definitions and modify only the necessary parameters, significantly reducing operational complexity.
3Measurement precision
If complete data classification pipelines are executed repeatedly, then result accuracy and completeness are improved, but computational resource usage increases
Solution Approach 1:
The system caches intermediate results (feature extractions, model predictions) from complete pipeline executions. When the same pipeline is run again, previously computed intermediate results are reused where applicable, maintaining result accuracy while reducing the computational resources required for repeated executions.
Solution Approach 2:
The system recovers and reuses valuable intermediate computation results from previous experiment executions. By identifying and reusing these cached results in subsequent experiments with similar workflows, the system avoids redundant computation while preserving the accuracy and completeness of the full classification pipeline.
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 defined graphically. 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.