Symbolic Data Structure for Targeted Query Extraction
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Solution Overview
Problem
Current data retrieval methods from databases are inefficient, particularly when dealing with large, unsorted datasets, as they require processing and filtering of extensive data, leading to increased computational resources and time.
Innovation Solution
The implementation of a data structure that represents data values using symbols, where data values satisfying a threshold are marked with a first symbol and those not satisfying it with a second symbol, allowing for efficient filtering and retrieval by applying filters to reduce the data set and improve query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data retrieval methods are used on large unsorted datasets, then complete data processing is achieved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent applies preliminary action by pre-processing the dataset before queries are executed. A data structure is constructed that marks data values with symbols indicating whether they satisfy certain thresholds. This preprocessing creates an indexed representation where filter applications become simple symbol-matching operations rather than full data scans, enabling fast retrieval without reprocessing the entire dataset for each query.
Solution Approach 2:
The patent extracts only the necessary information for filtering by creating a symbolic representation of data values. Instead of storing or processing all raw data values during queries, the system extracts threshold satisfaction information into a compact data structure with symbols, allowing queries to operate on this extracted representation rather than the complete dataset.
2Measurement precision
If all data values are processed to determine query results, then accurate results are obtained, but the amount of data extracted and processed is excessive
Solution Approach 1:
The patent extracts only the essential filtering information from the complete dataset. By creating a data structure that contains symbolic markers indicating threshold satisfaction for each data value, the system separates the necessary query-relevant information from the full dataset, allowing accurate query results to be obtained by processing only this extracted representation.
Solution Approach 2:
The patent applies local quality by treating different data values differently based on their threshold satisfaction properties. Each data value is marked with a symbol that encodes its relationship to threshold criteria, creating a heterogeneous representation where each element carries localized quality information relevant to specific query types, rather than treating all data uniformly.
3Ease of operation
If filters are applied to unsorted large datasets, then relevant data is identified, but computational resources are excessively consumed
Solution Approach 1:
The patent changes the parameter representation of data values by transforming raw data into symbolic markers that encode threshold satisfaction information. This parameter transformation converts complex filtering operations into simple symbol-matching operations, dramatically reducing computational resource consumption while maintaining filter application effectiveness.
Solution Approach 2:
The patent creates a simplified copy or representation of the data that captures only the essential filtering properties. Instead of working with the complete raw data during filter operations, the system uses this symbolic copy that replicates the threshold satisfaction information, allowing filters to operate efficiently on the copy rather than the original large dataset.
Data Source
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AI summary
In an aspect, provided is a method for targeted extraction of data satisfying a query. The method can rely upon one or more data structures indicative of data values satisfying or not satisfying one or more filters. Only the data indicated as satisfying (or as not satisfying) the data in a data structure related to the query are extracted. The extracted data may then be sorted.