Actionable Task Structures for Data Transformation Pipelines

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Solution Overview

Problem

Data scientists face challenges in efficiently applying and modifying transformation pipelines across disparate data sets and domains due to compatibility and operability issues, leading to significant resource wastage in identifying and resolving incompatibilities.

Innovation Solution

The implementation of a system that configures an existing transformation pipeline as a stored actionable task structure with predefined tasks, determines compatibility and operability based on task and data set attributes, and provides visual interfaces to reflect compatibility and suggest modifications, allowing for efficient identification and resolution of incompatibilities without requiring execution on the new data sets or domains.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If a transformation pipeline is customized for a specific data set with particular attributes, then the processing accuracy and effectiveness are improved, but the adaptability to other data sets with different attributes deteriorates

Engineering Contradiction:

Improvedata transformation accuracyVSAvoidpipeline reusability across different data sets

Core Design Contradiction:

Manufacturing precisionVSAdaptability or versatility

Solution Approach 1:

The transformation pipeline is divided into discrete, independently configurable tasks, each with defined input and output attributes. This segmentation allows individual tasks to be selected and configured based on specific data set requirements while maintaining overall pipeline structure, enabling both customization for accuracy and adaptability for reuse.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system creates a universal transformation pipeline framework that can handle multiple data set types through configurable tasks. By defining tasks with standardized attribute interfaces and using templates that can be parameterized for different domains, the pipeline achieves multi-functionality while maintaining precision for each specific application.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If a transformation pipeline is built from scratch for each new data set, then the processing effectiveness is improved, but the time and resources required deteriorate

Engineering Contradiction:

Improvedata transformation effectivenessVSAvoidtime to build and diagnose transformation pipelines

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary compatibility assessment by comparing data set attributes with pipeline task requirements before full execution. This preliminary action identifies potential incompatibilities early, allowing data scientists to modify the pipeline proactively rather than discovering issues during execution, thereby reducing overall time investment.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides feedback mechanisms that highlight attribute mismatches and compatibility issues between the data set and transformation pipeline tasks. This feedback enables data scientists to quickly identify and correct problems without extensive manual testing, reducing the time required to deploy effective transformation pipelines.

Inventive Principle:

Principle #23Feedback

3Productivity

If existing transformation pipelines are applied to new data sets without verification, then the productivity is improved, but the reliability of results deteriorates due to undetected incompatibilities

Engineering Contradiction:

Improvespeed of applying transformation pipelinesVSAvoidcompatibility assurance

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary compatibility verification by comparing data set attributes against pipeline task requirements before execution. This preliminary check ensures that only compatible pipelines are applied, guaranteeing reliability while maintaining productivity by preventing failed executions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system proactively identifies and prevents incompatibility issues before they affect transformation results. By checking attribute compatibility in advance and providing warnings or errors for mismatched pipelines, the system eliminates the risk of unreliable results while allowing rapid pipeline application.

Inventive Principle:

Principle #9Preliminary anti-action

Data Source

PatentUS10521448B2Application of actionable task structures to disparate data sets for transforming data in the disparate data sets

Publication Date: 2019.12.31 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Transformation pipelines are applied to disparate data sets and domains. An existing transformation pipeline is configured as a stored actionable task structure with a predefined plurality of sequenced tasks for transforming a first data set having a first set of attributes into a modified data set. The existing transformation pipeline is then applied to a different data set having a different set of attributes. Compatibility and operability for implementing the tasks of the actionable data set on the second data set are then determined, based at least in part on the stored attributes of the tasks and the second data set. Interface elements are also utilized to reflect the compatibility and operability of the tasks in the visual representation of the actionable task structure. Some elements are also utilized to provide suggestions for improving the compatibility of the actionable task structure.