Bloom Filter Indexing with Hamming Value for Database Search
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Solution Overview
Problem
Relational databases face inefficiencies in searching and indexing large datasets due to high overhead in maintaining multiple indexes, which can lead to full table scans and intrusive index maintenance, especially in extremely large systems, where a single index capable of detecting objects based on multiple properties is advantageous.
Innovation Solution
The implementation of a Bloom filter index system that creates a Bloom filter for each row in a database, using a Hamming value and binary logarithm to efficiently search and index multiple data columns, allowing for rapid detection of objects with specified properties and reducing the number of index scans required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple indexes are created to facilitate searching for various combinations of column values, then searching efficiency is improved, but device complexity and maintenance overhead increase
Solution Approach 1:
The patent combines multiple property-specific indexes into a single unified Bloom filter index structure. Instead of maintaining separate indexes for each column or property combination, the system creates one Bloom filter index that can handle queries for any combination of column values, thereby reducing device complexity and maintenance overhead while preserving searching efficiency.
Solution Approach 2:
The Bloom filter index is designed to be universal, serving multiple functions simultaneously. A single Bloom filter index can detect the presence of objects based on any combination of properties, replacing the need for multiple specialized indexes. This multi-functional approach reduces the overall system complexity while maintaining high searching efficiency across different query types.
2Device complexity
If a single Bloom filter index is used to detect objects based on multiple properties, then device complexity is reduced, but measurement precision may worsen due to false positives
Solution Approach 1:
The patent introduces Hamming values and binary logarithms as intermediary elements between the Bloom filter index and the final object detection result. These intermediaries provide additional filtering layers that help distinguish true positives from false positives, thereby improving measurement precision while maintaining the simplicity of the single Bloom filter index structure.
Solution Approach 2:
The system performs preliminary filtering using Hamming values and binary logarithms before final object detection. By pre-computing and storing these additional properties alongside the Bloom filter, the system can quickly eliminate false positives through preliminary comparisons, improving detection accuracy without adding complex index structures.
3Ease of operation
If linear search is used to locate a target Bloom filter in a collection, then ease of operation is maintained, but productivity decreases due to inefficient searching
Solution Approach 1:
The patent transforms the search space by introducing two new parameters: Hamming value and binary logarithm of the Bloom filter. By indexing objects based on these parameters, the system enables efficient parameter-based filtering that dramatically improves search speed while keeping the operation conceptually simple. Queries can quickly eliminate non-matching objects by comparing these parameters before performing full Bloom filter comparisons.
Data Source
AI summary
Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.