Database Query Processing Using Distance Statistics
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Solution Overview
Problem
Existing database query methods are inefficient for handling queries with character data, as they rely on minimum and maximum value statistics that are optimized for integer or floating-point entries, leading to high resource consumption and I/O operations.
Innovation Solution
Implement a method that uses distance statistics to pre-select datasets by calculating the similarity between a search value and reference values, reducing the number of datasets to search by determining a subset based on minimum and maximum distances, thereby minimizing I/O operations and computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If minimum and maximum value statistics are used for database queries, then resource consumption is reduced for integer or floating-point data, but the method does not work efficiently for character data
Solution Approach 1:
The patent changes the statistical parameter from minimum and maximum values (suitable for numeric data) to distance statistics (minimum and maximum distances from reference values), which is applicable to character data. This parameter transformation enables the same statistical filtering approach to work efficiently for both numeric and character types.
Solution Approach 2:
The distance statistics approach provides a universal method that can handle both integer/floating-point data and character data through the same mechanism. By using distance metrics from reference values, the system achieves multi-functionality across different data types without requiring separate optimization strategies.
2Reliability
If the whole database is searched for each query, then all data is covered, but resource consumption and I/O operations increase significantly
Solution Approach 1:
The patent performs preliminary computation of distance statistics (minimum and maximum distances from reference values) for each dataset during data loading or preprocessing. This preliminary action enables fast query filtering without computing distances during actual query execution, reducing I/O operations while maintaining search completeness.
Solution Approach 2:
The database is segmented into multiple datasets with individually computed distance statistics. During querying, the system segments the search space by identifying which datasets could potentially contain the search value based on distance criteria, avoiding unnecessary reading of entire database portions and reducing overall I/O operations.
3Measurement precision
If distance statistics are computed for multiple reference values, then query accuracy improves, but computational effort increases
Solution Approach 1:
The system computes distance statistics for multiple reference values in advance during data loading or preprocessing phases. This preliminary computation stores pre-calculated minimum and maximum distances, eliminating the need to compute these statistics during actual query execution, thus reducing real-time computational effort while maintaining high query accuracy.
Data Source
AI summary
The invention relates to a computer-implemented method for processing a query in a database, the query comprising a search value. The database comprises a plurality of datasets the datasets comprising entries, wherein distance statistics are assigned to the datasets. The distance statistics describe the minimum and maximum distance between the values of the entries of a dataset of the plurality of datasets and a reference value. The method comprises determining the distance between the search value and the reference value, said determination resulting in a search distance, determining a subset of datasets from the plurality of datasets for which the search distance is within the limits given by the minimum and maximum distances described by the respective distance statistics, and searching for the search value in the subset of datasets.