Multi-Dimensional Hierarchical Data Selection with Constraint Validation

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Solution Overview

Problem

In software applications with multiple dimensions, calculating valid dimensional combinations under constraints becomes computationally expensive due to the large number of potential combinations, leading to inefficiencies in data reduction and processing time.

Innovation Solution

The system reduces data options by identifying common dimensions, generating compatible node pairs, computing greatest lower bounds, and applying user-specific information to select a subset of options, thereby avoiding unnecessary comparisons and optimizing processing efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If every possible combination of dimensional points is examined to find valid combinations under constraints, then completeness of constraint validation is improved, but computational time and processing resources increase significantly

Engineering Contradiction:

Improveconstraint validation completenessVSAvoidcomputational time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent segments the dimensional space into hierarchical levels (parent nodes and child nodes) and processes constraints at different levels. Instead of examining all combinations uniformly, the system divides the search space into manageable segments based on the hierarchical structure, validating constraints at each level separately. This segmentation reduces the overall computational complexity while maintaining complete constraint validation.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs preliminary actions by pre-establishing valid parent node combinations and their relationships before final validation. The system pre-computes valid combinations at higher hierarchical levels and stores them for reference during lower-level validation. This preliminary computation eliminates the need to re-examine already-validated combinations, significantly reducing processing time while ensuring complete constraint validation.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If all dimensional combinations are generated and then filtered by constraints, then thoroughness of constraint application is improved, but the number of operations and processing complexity increases

Engineering Contradiction:

Improveconstraint application thoroughnessVSAvoidprocessing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

Instead of generating all combinations and then filtering by constraints (the conventional approach), the patent inverts the process by first establishing valid combinations through hierarchical constraint validation and only then generating the final set of valid dimensional points. This inversion reverses the logical sequence to reduce the search space before generation, thereby reducing processing complexity while maintaining thorough constraint application.

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The patent applies nesting by embedding constraint validation within the combination generation process itself. Valid parent node combinations are nested within the hierarchical structure, and child node combinations are generated only within the context of valid parent combinations. This nested approach ensures that constraints are applied at multiple levels simultaneously, reducing the overall number of operations while maintaining thorough constraint application.

Inventive Principle:

Principle #7Nested doll (Nesting)

3Ease of manufacture

If naive algorithms are used to search for intersections of constraint sets, then simplicity of implementation is improved, but processing efficiency deteriorates significantly

Engineering Contradiction:

Improveimplementation simplicityVSAvoidprocessing efficiency

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The patent introduces a hierarchical dimension to the constraint validation process, organizing dimensional points into parent-child relationships across multiple levels. Instead of treating all combinations equally in a flat structure, the system adds a hierarchical dimension that enables efficient pruning of invalid combinations. This dimensional change maintains implementation simplicity while dramatically improving processing efficiency by leveraging the hierarchical structure to reduce the search space.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS8549027B2Computer-implemented systems and methods for efficiently selecting data in multi-dimensional hierarchical space with multiple constraints

Publication Date: 2013.10.01 SAS INSTITUTE INC

AI summary

Computer-implemented systems and methods are provided for reducing a number of options presented to a user where the options have characteristics that are arranged in a multi-dimensional hierarchical space. A system and method can be configured to identify common dimensions using dimension nodes in dimensional points and identify compatible nodes for each dimension in the multi-dimensional hierarchical space by determining whether an association exists. Compatible pairs of surviving points are generated after applying each of the compatible nodes from the common dimensions. Greatest lower bounds (GLBs) are computed for each of the generated compatible points, and a set of options is generated comprising points from the generated compatible points and the computed GLBs.