Indexed Storage Structure for Efficient Data Retrieval
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Solution Overview
Problem
Existing data storage and retrieval methods, such as Bloom Filters and traditional database systems, are inefficient for quickly determining membership and additional characteristics of items, as they either consume excessive resources or return false positives, and cannot handle queries beyond membership verification without recreating the entire structure.
Innovation Solution
An indexed storage structure using two hash functions and a parity hash function is created, allowing for efficient retrieval of queried information, including membership and additional characteristics, with a compact size requirement, using an acyclic graphical representation and an algorithm that back-solves for variables to store and retrieve data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a Bloom Filter is used to store and retrieve membership information, then space efficiency and speed are improved, but the ability to determine additional characteristics about items is lost
Solution Approach 1:
The patent extends the Bloom Filter structure to serve multiple functions: it can determine membership status and simultaneously retrieve additional characteristics (such as zip code information) by storing multiple attributes at hash positions. This multi-functional approach allows a single data structure to replace both Bloom Filter and traditional database queries for membership verification with additional data retrieval.
Solution Approach 2:
The patent introduces a new dimension to the Bloom Filter by storing not just membership bits but also additional attribute data at the hash positions. This transforms the one-dimensional membership verification into a multi-dimensional structure that can retrieve both membership status and supplementary information (like zip codes) from the same data structure.
2Adaptability or versatility
If a traditional database system is used to store membership and additional characteristics, then the ability to determine additional characteristics is improved, but space efficiency and creation speed deteriorate
Solution Approach 1:
The patent merges the Bloom Filter membership verification mechanism with traditional database storage capabilities into a single unified structure. By combining the hash function-based compact storage with multi-attribute retrieval, it achieves both space efficiency and the ability to store additional characteristics without requiring separate database systems.
Solution Approach 2:
The patent changes the parameter storage approach from traditional database records to a compressed bit-array based on hash function outputs. This parameter transformation enables compact storage while maintaining the ability to retrieve multiple attributes by solving systems of linear equations derived from the hash functions.
3Adaptability or versatility
If a traditional database system is used, then the ability to store additional characteristics is improved, but creation time and resource usage deteriorate
Solution Approach 1:
The patent performs preliminary computation by pre-calculating hash function outputs and storing them in a compact array during the initial setup. This preliminary action enables rapid queries later without needing to recreate or scan large database structures, significantly reducing query time while maintaining the ability to store and retrieve additional characteristics.
Data Source
AI summary
Efficient data storage and retrieval (e.g., in terms of time and space requirements) is facilitated by implementing an indexing structure comprising an indexing array. That is, a functional relationship between elements of a source set and elements of a query result set can be stored in the indexing structure. This allows, for example, a query regarding whether an element is a member of a set (e.g., whether a particular website or Uniform Resource Locator (URL)) has been visited before) as well as a relationship between the member set and the query (e.g., the number of hyperlinks in the website the last time it was visited) to be resolved efficiently.