18048566. AUTOMATIC IDENTIFICATION OF MISSING VALUE PATTERNS FOR DIGITAL TWIN RESILIENCE SUPPORT simplified abstract (Dell Products L.P.)

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AUTOMATIC IDENTIFICATION OF MISSING VALUE PATTERNS FOR DIGITAL TWIN RESILIENCE SUPPORT

Organization Name

Dell Products L.P.

Inventor(s)

[[:Category:Herberth Birck Fr�hlich of Florianópolis (BR)|Herberth Birck Fr�hlich of Florianópolis (BR)]][[Category:Herberth Birck Fr�hlich of Florianópolis (BR)]]

Vinicius Michel Gottin of Rio de Janeiro (BR)

AUTOMATIC IDENTIFICATION OF MISSING VALUE PATTERNS FOR DIGITAL TWIN RESILIENCE SUPPORT - A simplified explanation of the abstract

This abstract first appeared for US patent application 18048566 titled 'AUTOMATIC IDENTIFICATION OF MISSING VALUE PATTERNS FOR DIGITAL TWIN RESILIENCE SUPPORT

Simplified Explanation

The patent application focuses on automatically identifying missing value patterns, imputing missing values, and using historical observations to find loss patterns in data.

Key Features and Innovation

  • Automatic identification of missing value patterns
  • Imputation of missing values
  • Search for loss patterns in historical observations
  • Utilization of loss patterns to find losses in online observations
  • Selection of imputation method based on loss pattern

Potential Applications

This technology can be applied in various fields such as data analysis, machine learning, and predictive modeling where missing data is a common issue.

Problems Solved

  • Addressing missing data in datasets
  • Improving data accuracy and reliability
  • Enhancing the efficiency of data analysis processes

Benefits

  • Enhanced data quality
  • Improved decision-making based on complete datasets
  • Time-saving in data cleaning and preprocessing tasks

Commercial Applications

Data Analysis Software for Businesses: This technology can be integrated into data analysis software used by businesses to improve the accuracy and reliability of their data analysis processes, leading to better decision-making.

Prior Art

There are existing methods for imputing missing values in datasets, but the innovation lies in the automatic identification of missing value patterns and the utilization of historical observations to find loss patterns.

Frequently Updated Research

There may be ongoing research in the field of data imputation and missing data handling techniques that could further enhance the capabilities of this technology.

Questions about Missing Value Patterns

How does this technology improve data analysis processes?

This technology enhances data analysis processes by automatically identifying missing value patterns and imputing missing values, leading to more accurate and reliable results.

What are the potential applications of this technology beyond data analysis?

This technology can be applied in various fields such as machine learning, predictive modeling, and any other domain where missing data is a common issue.


Original Abstract Submitted

Missing value patterns are automatically identified, and the missing values are imputed. Historical observations are searched for loss patterns, which include block loss patterns and row loss patterns. The loss patterns identified from the historical observations are used to find losses in online observations. Missing values are imputed using an imputation method that is selected based on the loss pattern.

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