Self-Compacting Pattern Indexer Graph Structure Reduces Data Redundancy
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Solution Overview
Problem
Conventional indexing methods, such as hierarchies and trees, lead to redundant data storage and inefficient data retrieval due to cross-references and semantic constraints, particularly in systems like databases and search engines, where data is often referenced and cross-referenced multiple times.
Innovation Solution
A graph-like data structure is employed to index and organize features, allowing for efficient clustering, splitting, and merging of nodes, which enables better storage and retrieval of data without redundant references, using a Self-Compacting Pattern Indexer (SCPI) to manage clusters of features as indices within the graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional hierarchy or tree structures are used for data indexing, then data can be organized with clear semantic constraints, but data redundancy increases and retrieval efficiency decreases due to multiple cross-references
Solution Approach 1:
The patent transitions from traditional one-dimensional hierarchical structures to a multi-dimensional graph structure where nodes can have multiple parents and children, enabling representation of complex relationships without redundancy. This dimensional change allows the same information to be accessed through multiple paths without being stored multiple times, resolving the contradiction between reliable organization and retrieval efficiency.
Solution Approach 2:
The graph structure serves multiple functions simultaneously: it maintains semantic constraints through node attributes and edge relationships, enables efficient retrieval through multiple access paths, and eliminates redundancy by allowing shared nodes to be referenced from multiple parents. This multi-functionality resolves the contradiction by making a single structure capable of both reliable organization and efficient retrieval.
2Adaptability or versatility
If hierarchical structures with cross-references are used, then semantic constraints can be enforced, but data storage redundancy increases
Solution Approach 1:
The patent merges multiple hierarchical structures into a single graph structure where common nodes are shared across different branches. Instead of storing the same information separately in multiple hierarchies, the graph structure allows different hierarchies to converge on shared nodes, reducing storage redundancy while maintaining semantic constraints through the graph's edge relationships and node attributes.
3Ease of manufacture
If conventional indexing methods are used, then data entry is straightforward, but data compression and retrieval efficiency are limited
Solution Approach 1:
The patent uses reference copying instead of data duplication. When the same information appears in multiple contexts, rather than copying the actual data multiple times (which would increase storage), the system creates reference copies that point to the same underlying node in the graph structure. This maintains ease of data entry while achieving high compression efficiency through shared references.
Data Source
AI summary
An indexing system uses a graph-like data structure that clusters features indexes together. The minimum atomic value in the data structure is represented as a leaf node which is either a single feature index or a sequence of two or more feature indexes when a minimum sequence length is imposed. Root nodes are formed as clustered collections of leaf nodes and/or other root nodes. Context nodes are formed from root nodes that are associated with content that is being indexed. Links between a root node and other nodes each include a sequence order value that is used to maintain the sequencing order for feature indexes relative to the root node. The collection of nodes forms a graph-like data structure, where each context node is indexed according to the sequenced pattern of feature indexes. Clusters can be split, merged, and promoted to increase the efficiency in searching the data structure.