Data Curation Resource Profiling for Efficient Task Allocation

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Solution Overview

Problem

Inefficient allocation of data curation resources leads to unavailability and reduced quality of curated data, affecting the reliability and performance of computer-implemented services due to mismatched proficiency levels of data curation resources with the types of data they are assigned to curate.

Innovation Solution

A system and method for optimizing data curation resource allocation by analyzing historical performance data to create resource profiles, matching data types with the most proficient data curation resources, and assigning tasks based on fitness values to ensure efficient and high-quality data curation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If data curation resources are allocated without considering proficiency levels, then allocation process is simple and fast, but data curation quality and reliability deteriorate

Engineering Contradiction:

Improvedata curation efficiencyVSAvoiddata curation quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary actions by analyzing historical performance data and creating resource profiles before actual data curation tasks are assigned. This preliminary profiling enables the system to make informed matching decisions between data types and resources based on proven proficiency levels, thereby improving both efficiency and quality without requiring complex real-time assessments during task execution

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system utilizes feedback mechanisms by analyzing historical performance data from previous data curation tasks to create and update resource profiles. This feedback loop allows the system to continuously improve its matching accuracy by learning from past performance, ensuring that resources are consistently assigned to data types based on demonstrated proficiency rather than assumptions

Inventive Principle:

Principle #23Feedback

2Reliability

If data curation resources are matched based on proficiency levels, then data curation quality improves, but resource allocation complexity increases

Engineering Contradiction:

Improvedata curation qualityVSAvoidresource allocation system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system implements self-service by automatically analyzing historical performance data and generating resource profiles without requiring manual intervention or complex external tools. The automated profiling process simplifies the overall system complexity while maintaining high matching accuracy, as the system serves its own needs for resource characterization using its existing computational capabilities

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system manages complexity by transforming the multi-dimensional problem of resource matching into a simplified parameter-based approach. By changing the representation of resource capabilities into structured profiles with key parameters (such as proficiency levels for different data types), the system reduces the complexity of allocation decisions while improving matching quality

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If historical performance data is analyzed to create resource profiles, then resource matching accuracy improves, but processing time and computational resources increase

Engineering Contradiction:

Improveresource matching accuracyVSAvoidprofile generation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs the time-consuming profile analysis as a preliminary action during off-peak times or in batches, rather than in real-time during task assignment. This allows the system to pre-compute resource profiles and make quick matching decisions during actual data curation operations, separating the heavy computational workload from the time-critical assignment process

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies partial action by analyzing only the most relevant historical performance data and parameters needed for accurate matching, rather than processing all available historical information. This selective approach maintains high matching accuracy while reducing processing time and computational resource consumption by focusing on the most impactful factors

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250004854A1Prioritizing curation targets for data curation based on resource proficiency

Publication Date: 2025.01.02 DELL PROD LP

AI summary

Methods and systems for curating data by a data manager are disclosed. Data collected from various data sources may be curated before being provided to downstream consumers that may rely on the trustworthiness of the curated data in order to provide computer-implemented services. During data curation, data curation resources may be assigned to curate (e.g., improve the trustworthiness of) the data. However, the data curation resources (e.g., data curators) may have differing abilities (e.g., levels of efficiency) for curating different types of data; therefore, the efficiency of the data curation process may depend on the strengths and/or weaknesses of the data curation resource assigned to curate a data type. Inefficient data curation may lead to an unavailability of trustworthy data for downstream consumers; thus, to optimize the allocation of data curation resources, the data type may be matched with the data curation resource(s) likely to curate the data most efficiently.