17917083. Meta-Estimation of Bloom Filters with Vectors of Counts simplified abstract (Google LLC)

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Meta-Estimation of Bloom Filters with Vectors of Counts

Organization Name

Google LLC

Inventor(s)

Preston Wooju Lee of Bellevue WA (US)

Craig William Wright of Louisville CO (US)

Joseph Sean Cahill Goodknight Knightbrook of Santa Monica CA (US)

Evgeny Skvortsov of Kirkland WA (US)

Meta-Estimation of Bloom Filters with Vectors of Counts - A simplified explanation of the abstract

This abstract first appeared for US patent application 17917083 titled 'Meta-Estimation of Bloom Filters with Vectors of Counts

Simplified Explanation

The patent application describes systems and methods for estimating data structures representing identifiers. Here are the key points:

  • The system maintains data records that include identifiers and attributes.
  • Using these data records, the system generates a first data structure (e.g., probabilistic data structure) that represents the data records.
  • The first data structure consists of multiple registers.
  • The system identifies a subset of registers that have a predetermined value.
  • A second data structure is generated to represent this subset of registers.
  • The second data structure is stored as a meta-estimation of the first data structure.
  • The second data structure can be used in further processing operations.

Potential applications of this technology:

  • Data analysis and processing: The system can be used to efficiently estimate and process large amounts of data records with identifiers and attributes.
  • Database management: The technology can enhance the performance and storage efficiency of databases that handle large datasets.
  • Machine learning: The meta-estimation of data structures can be utilized in various machine learning algorithms to improve efficiency and accuracy.

Problems solved by this technology:

  • Efficient representation of data: The second data structure provides a more compact representation of the original data records, reducing storage requirements.
  • Faster processing: By utilizing the second data structure, processing operations can be performed more quickly, improving overall system performance.
  • Scalability: The technology allows for efficient handling of large datasets, enabling scalability in data-intensive applications.

Benefits of this technology:

  • Improved storage efficiency: The meta-estimation technique reduces the storage space required for representing data records.
  • Faster data processing: Utilizing the second data structure speeds up processing operations, leading to quicker results.
  • Scalability and flexibility: The technology can handle large datasets and can be applied to various domains, making it adaptable to different use cases.


Original Abstract Submitted

Systems and methods for the meta-estimation of data structures representing identifiers are disclosed. The system maintain one or more data records comprising one or more identifiers and one or more attributes. Using the data records, the system can generate a first data structure, such as a probabilistic data structure, that represents the plurality of data records. The first data structure can have a plurality of registers. The system can identify a subset of the plurality of registers that are equal to a predetermined value, and generate a second data structure that represents the subset of the plurality of registers. The system can then store the second data structure as a meta-estimation of the first, and can utilize the second data structure in further processing operations.

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