17850764. SYSTEM AND METHOD FOR CAPACITY PLANNING FOR DATA AGGREGATION USING SIMILARITY GRAPHS simplified abstract (Dell Products L.P.)

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SYSTEM AND METHOD FOR CAPACITY PLANNING FOR DATA AGGREGATION USING SIMILARITY GRAPHS

Organization Name

Dell Products L.P.

Inventor(s)

Ofir Ezrielev of Be'er Sheva (IL)

Jehuda Shemer of Kfar Saba (IL)

SYSTEM AND METHOD FOR CAPACITY PLANNING FOR DATA AGGREGATION USING SIMILARITY GRAPHS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17850764 titled 'SYSTEM AND METHOD FOR CAPACITY PLANNING FOR DATA AGGREGATION USING SIMILARITY GRAPHS

Simplified Explanation

Methods and systems for managing data aggregation in a distributed environment are disclosed. The invention involves using twin inference models to reduce the amount of data transmitted for aggregation purposes. These twin inference models are trained using computing resources. The patent application describes estimating the computing resource cost for training the twin inference models based on the number of models needed to achieve desired inference accuracy goals. A model training device with sufficient computing resources is obtained and used to train and distribute the inference models for data aggregation.

  • Twin inference models are used to aggregate data in a distributed environment.
  • The twin inference models help reduce the amount of data transmitted for aggregation.
  • Computing resource cost for training the twin inference models is estimated based on desired inference accuracy goals.
  • A model training device with sufficient computing resources is obtained to train and distribute the inference models.

Potential Applications

  • Data aggregation in distributed systems.
  • Internet of Things (IoT) applications.
  • Big data analytics.
  • Machine learning and artificial intelligence.

Problems Solved

  • Reducing the amount of data transmitted for aggregation.
  • Efficient utilization of computing resources.
  • Meeting inference accuracy goals in a distributed environment.

Benefits

  • Improved efficiency in data aggregation.
  • Reduced network bandwidth usage.
  • Cost-effective utilization of computing resources.
  • Enhanced accuracy in inference models.


Original Abstract Submitted

Methods and systems for managing data aggregation in a distributed environment are disclosed. The data may be aggregated using twin inference models which may be used to reduce a quantity of data transmitted to aggregate the data. To obtain twin inference models, models may be trained which may consume computing resources. A computing resource cost for training the twin inference models may be estimated based on an estimated number of twin inferences models necessary to meet inference accuracy goals. A model training device that has an available quantity of computing resources sufficient to meet the computing resource cost may be obtained. The model training device may be used to train and distribute inference models for data aggregation purposes.

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