Hierarchical Query Processing for Large Data Sets
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Solution Overview
Problem
Existing systems face inefficiencies in processing large data sets, leading to prolonged query times due to the need for numerous computations, which is unacceptable in today's fast-paced environment.
Innovation Solution
The system employs a hierarchical approach by performing computations on high-level elements, sorting, and recursively processing sub-elements until a specified level of granularity is reached, significantly reducing the number of computations required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computations are performed on all elements in large data sets, then complete accuracy is achieved, but query processing time becomes excessively long
Solution Approach 1:
The patent segments the data set into elements and sub-elements, organizing them in a hierarchical structure. Computations are first performed on aggregate data values of high-level elements to identify a subset, then recursively applied to sub-elements of selected elements. This segmentation allows the system to process only relevant portions of the data at each level, dramatically reducing total computation time while maintaining accuracy for the selected subset.
Solution Approach 2:
The patent performs preliminary computations on aggregate data values of high-level elements before processing individual sub-elements. By first evaluating elements at a higher level of abstraction and selecting a subset based on these preliminary results, the system avoids performing computations on all sub-elements from the outset. This preliminary filtering action significantly reduces the scope of subsequent detailed processing.
2Measurement precision
If computations are performed on all sub-elements, then complete result accuracy is achieved, but the number of computations becomes unmanageably large
Solution Approach 1:
The hierarchical segmentation of data into elements and sub-elements enables the system to perform computations at multiple levels. By first computing aggregate values for elements and then selectively processing sub-elements of only those elements that meet selection criteria, the patent reduces the total number of computations required while maintaining result accuracy for the selected subset.
Solution Approach 2:
The patent applies partial action by performing computations on a subset of elements and their sub-elements rather than all elements in the data set. The system selects a subset of elements based on aggregate computation results, and then performs detailed computations only on sub-elements of the selected elements, achieving sufficient accuracy for the query without the excessive computational burden of processing everything.
3Loss of time
If a hierarchical processing approach is used, then computation time is reduced, but system complexity increases
Solution Approach 1:
While segmentation into hierarchical levels does increase structural complexity, it dramatically reduces computational complexity by dividing the problem into manageable segments. The system maintains element-subelement relationships and aggregate data values that enable efficient recursive processing, trading organizational complexity for substantial gains in processing efficiency.
Solution Approach 2:
The patent implements dynamic processing where the level of detail and depth of recursion adapts based on selection criteria and intermediate results. The system can dynamically adjust which elements and sub-elements require detailed processing, making the complexity management flexible and efficient rather than rigid and uniform.
Data Source
AI summary
Described herein are a system and techniques for increasing the efficiency of generating a result set for a query. In some embodiments, the techniques may involve performing computations on a high-level element, sorting, and selecting a set of the high-level elements, and recursively repeating the process on sub-elements of the set of the high-level elements. The process may be recursively repeated until a specified level of granularity is reached. This may significantly decrease the number of computations that need to be performed, increasing the speed with which queries can be performed. In some embodiments, the process may involve identifying elements which may be highly correlated to optimal computation results and may add those elements to the result set to improve the accuracy of the result set.