17668312. TWO-LEVEL INDEXING FOR KEY-VALUE PERSISTENT STORAGE DEVICE simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

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TWO-LEVEL INDEXING FOR KEY-VALUE PERSISTENT STORAGE DEVICE

Organization Name

SAMSUNG ELECTRONICS CO., LTD.

Inventor(s)

Omkar Desai of Syracuse NY (US)

Changho Choi of San Jose CA (US)

Yangwook Kang of San Jose CA (US)

TWO-LEVEL INDEXING FOR KEY-VALUE PERSISTENT STORAGE DEVICE - A simplified explanation of the abstract

This abstract first appeared for US patent application 17668312 titled 'TWO-LEVEL INDEXING FOR KEY-VALUE PERSISTENT STORAGE DEVICE

Simplified Explanation

The patent application describes a system and method for two-level indexing for key-value persistent storage. This involves sorting key-value pairs, determining addresses for each pair, and training a linear regression model to generate a line corresponding to the pairs.

  • The method involves sorting two or more key-value pairs to create a sorted set.
  • It determines the addresses of the key-value pairs.
  • It trains a linear regression model using the first and second key-value pairs.
  • The linear regression model generates a line corresponding to the key-value pairs.

Potential Applications

  • This technology can be applied in databases and storage systems that use key-value pairs.
  • It can improve the efficiency and performance of key-value storage systems.

Problems Solved

  • The two-level indexing approach helps in organizing and accessing key-value pairs more efficiently.
  • It addresses the problem of slow retrieval and storage of key-value pairs in large databases.

Benefits

  • The use of two-level indexing improves the speed and efficiency of key-value storage systems.
  • It allows for faster retrieval and storage of data.
  • The linear regression model helps in predicting the location of key-value pairs, further enhancing performance.


Original Abstract Submitted

A system and method for two-level indexing for key-value persistent storage. The method may include: sorting two or more key-value pairs to form a sorted key-value pair set; determining an address of a first key-value pair of the key-value pairs, the first key-value pair including a first key and a first value; determining an address of a second key-value pair of the key-value pairs, the second key-value pair including a second key and a second value; and training a first linear regression model to generate a first line corresponding to the key-value pairs, the training including training the first linear regression model with key-value pairs including the first key-value pair and the second key-value pair.

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