Multi-Level Bloom Filter for Reducing False Positives
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Solution Overview
Problem
Existing data search methods in large databases face inefficiencies due to increasing access time as the database size grows, and current solutions like hash tables and binary search algorithms incur costly memory requirements and false positives.
Innovation Solution
An enhanced filter system using multiple levels and multidimensional data structures with hash functions and string manipulation operations to reduce hashing collisions and false positive probabilities, allowing for adaptable filter parameters based on context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hash tables are used to improve search speed, then access time is reduced, but memory requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for search operations from the full database, using Bloom filters to store condensed data representations. This allows fast search without requiring full database access, resolving the contradiction between search speed and memory requirements.
Solution Approach 2:
The patent creates a simplified copy of the database structure using Bloom filters, which stores hashed representations of data rather than actual data. This copy enables fast search operations with minimal memory footprint, addressing both speed and memory constraints.
2Quantity of substance
If binary search algorithms are used to reduce memory usage, then memory requirements are lowered, but search time increases
Solution Approach 1:
The patent pre-computes and stores hashed representations of data elements in Bloom filters before search operations. This preliminary processing enables O(1) average case search time while using minimal memory, overcoming the trade-off between memory usage and search speed.
Solution Approach 2:
The patent changes the data representation parameters by using probabilistic data structures (Bloom filters) instead of traditional sorted arrays. This parameter change enables both low memory usage and fast search by utilizing hash functions and bit array operations.
3Measurement precision
If probabilistic filters are used to reduce false positives, then accuracy is improved, but false positive probability remains non-zero
Solution Approach 1:
The patent extends the Bloom filter structure to multiple dimensions by applying multiple hash functions and organizing data in multi-level Bloom filters. This dimensional expansion significantly reduces the false positive probability while maintaining the probabilistic nature of the structure, improving both accuracy and reliability.
Solution Approach 2:
The patent combines multiple Bloom filter structures with different parameters and multiple hash functions into a composite filtering system. This composite approach leverages the strengths of individual filters while mitigating their weaknesses, achieving higher accuracy with lower false positive rates.
Data Source
AI summary
Enhanced filter accuracy is disclosed with respect to determining whether a data element is present in a data store. The filter can correspond to a probabilistic filter with multiple levels. At each level after a representation of an original data element is captured in a data structure, a predetermined modification can be applied to the data element and a representation of the resulting modified data element can be captured in the data structure. In query operation, a check be performed for the presence or absence of an input data element and a number of modifications of the input data element, which results in a reduced false positive probability and improved accuracy. A multidimensional data structure can also be employed in conjunction with multiple levels to further improve accuracy.