Range-Based Data Block Search for Database Efficiency
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Solution Overview
Problem
Existing database search methods are inefficient as they require examining each individual record, leading to slow search processes and high computing resource demands, especially in large databases, as they typically rely on fixed value searching rather than range searching.
Innovation Solution
The proposed method organizes data into blocks representing values within specified ranges for characteristics, using metadata to quickly identify relevant blocks and subblocks during searches, thereby reducing the need to check every individual record by utilizing conventional record-by-record search methods only on a restricted dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional record-by-record search methods are used, then comprehensive data coverage is achieved, but search time and computing resources increase significantly
Solution Approach 1:
The patent segments the database into multiple data blocks, each representing a specific range of values for one or more characteristics. During search operations, the system divides the search space into these blocks and only examines blocks that may contain matching records, rather than checking every individual record. This segmentation dramatically reduces the number of records that need to be examined while ensuring comprehensive coverage of all potential matches.
Solution Approach 2:
The patent performs preliminary organization of data into blocks with predetermined characteristic ranges during the database setup phase. Metadata is pre-calculated and stored for each block, describing the range of values contained within. This preliminary action allows the search system to quickly identify relevant blocks without having to analyze individual records during the search operation, thereby reducing search time while maintaining comprehensive data coverage.
2Reliability
If conventional record-by-record search methods are used, then all matching records are identified, but computing resource demands increase
Solution Approach 1:
By segmenting the database into value-range blocks and pre-calculating metadata for each block, the system avoids the need to examine every individual record during search operations. The metadata allows the system to identify and skip blocks that cannot contain matching records, significantly reducing CPU cycles and computing resource consumption while still identifying all matching records.
Solution Approach 2:
The patent extracts and stores metadata describing the characteristic ranges for each data block separately from the actual record data. This extracted metadata is used during search operations to quickly determine which blocks need to be examined, eliminating the need to process metadata for every individual record and thereby reducing computing resource demands.
3Speed
If data is stored in blocks with metadata representing value ranges, then search speed increases, but data storage structure complexity increases
Solution Approach 1:
The database is segmented into blocks, each associated with metadata describing the range of values for one or more characteristics contained in that block. This segmentation enables the search system to quickly identify relevant blocks by comparing query parameters against block metadata, dramatically improving search speed. The added structural complexity is localized to the metadata layer, while the actual data storage remains organized in manageable blocks.
Solution Approach 2:
The block-based structure with metadata serves multiple functions: it enables fast range-based search by allowing quick identification of relevant blocks, maintains comprehensive data coverage by ensuring all records are assigned to appropriate blocks, and supports flexible querying across different characteristic ranges. This multi-functional design justifies the additional structural complexity by delivering significant performance benefits.
4Adaptability or versatility
If range-based search is implemented instead of fixed value search, then query flexibility improves, but search algorithm complexity increases
Solution Approach 1:
The search algorithm benefits from preliminary organization of data into blocks with pre-calculated metadata describing value ranges. During a search operation, the algorithm simply compares the query's characteristic ranges against the stored block metadata to identify relevant blocks. This preliminary action transforms a potentially complex range-based search problem into a simple comparison operation, maintaining algorithm simplicity while enabling flexible range-based queries.
Solution Approach 2:
The patent introduces a new dimension to the search process by organizing data along characteristic value ranges rather than individual record boundaries. The metadata adds a dimensional layer that describes the range space covered by each block, allowing the search algorithm to operate in this expanded dimension space. This dimensional change enables flexible range-based queries while keeping the algorithm relatively simple, as it only requires comparing query ranges against block ranges.
Data Source
AI summary
This invention discloses a novel method for storing data in virtual multidimensional blocks and accessing and retrieving desired information from these blocks. Specific items of data whose characteristics fall within the range of a specified block are stored within that block. Blocks with smaller ranges are nested within larger blocks with the same characteristics. This invention's search method involves checking the specific range of a search query against the largest relevant block range, and then successively checking smaller and smaller range blocks that contain the desired data. This method provides greater speed and accuracy than conventional database linear storage and record by record search methods.