Multidimensional Query Processing with Partitioned Hypercube Lookup
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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 system 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, supporting unlimited dimensions and rapid calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users define dataset intersections in high-dimensional hypercubes, then query evaluation can be performed, but users require advanced knowledge and the system performs redundant calculations
Solution Approach 1:
The patent segments the complex query evaluation process into distinct components: query parsing, dimension processing, coordinate tuple generation, and data retrieval. By dividing the monolithic query evaluation system into modular segments, the system reduces operational complexity for users while maintaining comprehensive query capabilities across high-dimensional hypercubes
Solution Approach 2:
The patent introduces intermediary components including query parsers that translate user-friendly queries into internal representations, and coordinate tuple generators that mediate between dimension specifications and actual data retrieval. These intermediaries shield users from the underlying complexity of high-dimensional data structures while enabling efficient query evaluation
2Adaptability or versatility
If the hypercube dimensions and data volume increase, then the model becomes more comprehensive, but query evaluation time and computational resources increase
Solution Approach 1:
The patent implements preliminary action through pre-computation of coordinate tuples and aggregation of dimension data before queries are executed. By pre-processing and storing intermediate results in optimized data structures, the system reduces the computational burden during actual query evaluation, enabling rapid response even for high-dimensional comprehensive models
Solution Approach 2:
The patent transforms the high-dimensional hypercube query problem into a series of lower-dimensional processing tasks by decomposing queries into coordinate tuples across multiple dimensions. This dimensional decomposition allows the system to handle comprehensive high-dimensional models efficiently by processing each dimension independently and combining results
3Productivity
If the system processes queries across multiple dimensions with hierarchical attributes, then analysis capability improves, but redundant calculations increase
Solution Approach 1:
The patent merges redundant calculation operations by identifying and consolidating common sub-expressions across multiple queries. When the same dimension aggregations or coordinate computations are required by different queries, the system combines these operations into single computational passes, reducing redundant energy consumption while maintaining comprehensive multi-dimensional analysis capability
Solution Approach 2:
The patent implements computation caching that discards redundant intermediate calculation results and recovers them for future use. By storing and reusing previously computed dimension aggregations and coordinate tuples, the system eliminates repeated calculations across multiple queries, significantly reducing computational energy requirements while preserving full analytical capability
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.