Attribute-Relationship Clustering for Hierarchical Search Results
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Solution Overview
Problem
Traditional search systems lack dynamic clustering based on entity attributes and relationships, leading to difficulties in organizing and analyzing search results, identifying patterns, and addressing errors, as they often rely on static folder structures and non-hierarchical clustering.
Innovation Solution
A system that dynamically clusters search results based on entity attributes and relationships, presenting them in a hierarchical tree structure, allowing for multiple views and enabling actions based on relationship attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static folder structures are used for organizing search results, then the system structure is simple and easy to implement, but the results are not dynamically organized and patterns cannot be effectively identified
Solution Approach 1:
The patent implements dynamic clustering that automatically reorganizes search results based on entity attributes and relationships. The system transitions from static folder structures to dynamic clusters that adapt to the data being searched, allowing results to be automatically grouped by relevant attributes without manual intervention.
Solution Approach 2:
The patent adds a new dimension of organization by introducing hierarchical clustering based on entity attributes and relationships. This creates multiple levels of organization (clusters within clusters) that go beyond traditional flat folder structures, enabling pattern identification across different dimensions of the data.
2Adaptability or versatility
If traditional non-hierarchical clustering is used, then the implementation is simpler, but hierarchical relationships between entities cannot be formed
Solution Approach 1:
The patent implements nested clustering where clusters can contain sub-clusters, creating a hierarchical structure. This allows entities to be organized at multiple levels of abstraction, with parent clusters containing child clusters that provide more detailed groupings based on specific attributes and relationships.
Solution Approach 2:
The hierarchical clustering structure is dynamically generated based on the search results and entity relationships. The system automatically determines the hierarchy based on the data characteristics rather than using a fixed predefined structure, allowing the hierarchy to adapt to different search scenarios.
3Productivity
If manual folder creation is used (like Microsoft inbox), then user control is high, but it is difficult to figure out errors in data and analyze patterns
Solution Approach 1:
The system performs automatic clustering based on entity attributes and relationships without requiring manual folder creation. The clustering algorithm autonomously analyzes the search results and organizes them into meaningful groups, eliminating the need for users to manually create and manage folder structures while enabling automatic pattern identification.
Solution Approach 2:
The system provides feedback about data patterns and relationships through the automated clustering structure. By organizing results hierarchically based on attributes and relationships, the system makes patterns visible and allows users to identify errors and anomalies more easily through the structured presentation.
4Adaptability or versatility
If traditional faceted search is used, then users can explore results by attributes, but dynamic clustering based on entity relationships is not provided
Solution Approach 1:
The patent merges faceted search capabilities with relationship-based clustering. The system combines attribute-based filtering with entity relationship analysis, creating clusters that consider both the attributes of individual entities and their relationships to other entities, providing a more comprehensive organization approach.
Solution Approach 2:
The clustering system serves multiple functions: it organizes results by attributes, establishes hierarchical relationships, identifies patterns, and enables error detection. This multi-functional approach replaces the need for separate faceted search and relationship analysis tools with a unified clustering system.
Data Source
AI summary
Systems and methods for searching digital information dynamically clusters assets based on attributes and/or relationships and shows them in folders. Each cluster level may be based on a different attribute. The basis of cluster formation may be defined by a user. In some embodiments, multiple such views can be defined, and users can switch between different views. Embodiments help users to find insights on data, as well as help users to find errors in data. Actions can be automatically taken based on clustered search results.