Hypercube Query Evaluation with Parallel Subqueries and Cached Dependencies
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Solution Overview
Problem
Existing query evaluation systems struggle with large and complex models, particularly when dealing with high-dimensional hypercubes, requiring users to have advanced knowledge to define dataset intersections and performing redundant calculations.
Innovation Solution
A query evaluation service that allows users to describe attributes of responsive data intuitively, deconstructs queries into independent subqueries for parallel processing, and stores dependencies in memory to reduce redundant calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If query evaluation systems use traditional multi-dimensional databases to handle large and complex models, then they can store and organize data effectively, but they require users to have advanced knowledge to define dataset intersections and perform redundant calculations
Solution Approach 1:
The patent introduces an intermediary layer between the user and the complex multi-dimensional database system. This intermediary automatically translates intuitive user queries into the appropriate dataset intersections and coordinate tuples, eliminating the need for users to have advanced knowledge of the underlying data structure while preventing redundant calculations through intelligent query optimization.
Solution Approach 2:
The system performs self-service by automatically optimizing query execution without requiring user intervention. It autonomously determines the most efficient way to evaluate queries across the multi-dimensional space, manages memory allocation for intermediate results, and handles coordinate transformations, thereby reducing operational complexity for users.
2Productivity
If the system processes queries across high-dimensional hypercubes with thousands or millions of coordinates, then it provides comprehensive data analysis, but it increases computation time and memory usage
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing intermediate results in memory before they are needed for final query evaluation. When a query is received, the system checks for previously computed results that can be reused, significantly reducing computation time for repeated or related queries across the high-dimensional space.
Solution Approach 2:
The system applies local quality by optimizing memory allocation and computation resources based on the specific requirements of each query region in the multi-dimensional space. Instead of uniformly processing the entire hypercube, it focuses computational efforts on the relevant local regions identified by the query coordinates, reducing overall processing time and memory usage.
3Loss of time
If the system stores intermediate results in memory to eliminate redundant calculations, then it reduces computation time, but it increases memory usage
Solution Approach 1:
The patent applies partial action by selectively storing only the most frequently accessed or most beneficial intermediate results in memory, rather than caching all possible computations. The system intelligently identifies which coordinate tuples and query results provide the greatest time-saving benefit, storing only those to eliminate the most significant redundant calculations while maintaining acceptable memory usage.
4Productivity
If the system deconstructs queries into independent subqueries for parallel processing, then it improves processing efficiency, but it increases the complexity of query management
Solution Approach 1:
The patent applies segmentation by automatically dividing complex queries into independent subqueries that can be processed in parallel across different compute resources. The system manages this segmentation transparently, handling the coordination and aggregation of subquery results without requiring user involvement, thereby improving productivity while masking the underlying complexity.
Data Source
AI summary
A system, method, and device for evaluating a query is provided. The method includes (i) receiving a query that is indicative of one or more properties of result data, (ii) determining, based at least in part on the query, one or more partitions of a hypercube that are expected to comprise information used in generating result data for a response for the query; (iii) generating a data structure comprising a set of data of the one or more partitions, wherein the data structure is indicative of relationships among data in the set of data; (iv) evaluating the query with respect to the plurality of account groups; and (v) using the data structure to determine the result data based at least in part on an evaluation of the query with respect to the plurality of account groups.