20240013456. COMPUTERIZED SYSTEMS AND METHODS FOR GRAPH DATA MODELING simplified abstract (Yahoo Assets LLC)

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COMPUTERIZED SYSTEMS AND METHODS FOR GRAPH DATA MODELING

Organization Name

Yahoo Assets LLC

Inventor(s)

Travis Adam Walker of New York NY (US)

Mohammad Suhale Malang Khader of New York NY (US)

COMPUTERIZED SYSTEMS AND METHODS FOR GRAPH DATA MODELING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240013456 titled 'COMPUTERIZED SYSTEMS AND METHODS FOR GRAPH DATA MODELING

Simplified Explanation

The patent application describes a method for graph data modeling, which involves receiving raw data and determining a model for the raw data. The model defines the graph structure for the raw data. The raw data is then converted to fit the model, and a graph is generated based on the raw data and the model. The graph produces modeled data, which is then archived.

  • The method involves receiving raw data and determining a model for the raw data.
  • The model defines the graph structure for the raw data.
  • The raw data is converted to fit the model.
  • A graph is generated based on the raw data and the model.
  • The graph produces modeled data.
  • The modeled data is archived.

Potential Applications:

  • Data analysis and visualization: The technology can be used to model and analyze complex data sets, allowing for better understanding and visualization of relationships within the data.
  • Network analysis: The graph structure can be used to analyze and optimize networks, such as social networks or computer networks.
  • Recommendation systems: The modeled data can be used to generate personalized recommendations based on user preferences and relationships within the data.

Problems Solved:

  • Complex data modeling: The technology provides a method for modeling and structuring complex data sets, making it easier to analyze and extract meaningful insights.
  • Data integration: The conversion of raw data to fit the model allows for the integration of disparate data sources into a unified graph structure.
  • Data archiving: The technology includes archiving the graph, ensuring that the modeled data is preserved for future analysis and reference.

Benefits:

  • Improved data analysis: The graph structure allows for more efficient and effective analysis of complex data sets, leading to better insights and decision-making.
  • Enhanced data integration: The conversion of raw data to fit the model enables the integration of diverse data sources, providing a more comprehensive view of the data.
  • Long-term data preservation: Archiving the graph ensures that the modeled data is preserved for future use, allowing for historical analysis and comparison.


Original Abstract Submitted

systems, methods, and computer-readable media are provided for graph data modeling. in accordance with one implementation, a method is provided that includes operations performed by at least one processor. the operations of the method include receiving raw data and determining a model for the raw data, wherein the model defines the graph structure for the raw data. the method also includes converting the raw data to fit the model, and generating at least a portion of a graph based on the raw data and the model, wherein the graph produces modeled data. the method also includes archiving the graph.

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