Graph Database Visualization Engine for Large Data Volumes
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Solution Overview
Problem
Conventional visualization methods for graph databases become overwhelmed with large volumes of data, making it difficult to understand and navigate, especially on small screen sizes, as they typically represent millions of nodes and edges, which is not intuitively comprehensible.
Innovation Solution
An engine processes graph data to create metadata identifying node and edge types, allowing for simplified visualization and querying, enabling users to switch between overview and detailed node-level views, and converting query results into tabular form for analysis with relational database tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional graph visualization methods are used to represent large volumes of graph data, then the complete graph structure can be displayed, but the visualization becomes overwhelming and difficult to understand and navigate
Solution Approach 1:
The patent segments the graph visualization into multiple levels: an overview level showing aggregated node types and edge types, and a detailed level showing individual nodes and edges. This segmentation allows users to navigate from a simplified overview to detailed views only when needed, resolving the contradiction between displaying complete data and maintaining ease of operation.
Solution Approach 2:
The patent extracts and aggregates node and edge data into metadata representations at the overview level. Instead of displaying all individual nodes and edges, the system extracts the essential structure (node types and edge types) to create a simplified representation that maintains the graph's organizational information while removing overwhelming detail.
2Quantity of substance
If conventional graph visualization methods are used to represent large volumes of graph data, then the complete graph structure can be displayed, but the visualization becomes overwhelming on small screen sizes
Solution Approach 1:
The patent segments the visualization into overview and detailed views, allowing the overview to occupy minimal screen space while containing all necessary structural information. Users can expand to detailed views only when needed, making the system adaptable to small screen sizes while maintaining complete data representation capability.
3Loss of information
If graph data is visualized at detailed node level, then complete information can be viewed, but query formulation and data exploration become more difficult
Solution Approach 1:
The patent segments the interface into overview and detailed views, allowing users to form queries at the abstract node type level in the overview, then drill down to detailed node-level information only when needed. This maintains information completeness while significantly ease of query formulation.
Solution Approach 2:
The patent performs preliminary aggregation of graph data into node type and edge type metadata at the overview level. This preliminary organization of information allows users to formulate queries without needing to navigate through individual nodes, making query formulation easier while preserving complete information access.
Data Source
AI summary
Embodiments provide for querying and visualization of query results of graph data. An engine processes graph data to create metadata (e.g., in JSON format) identifying at least different node types and edge types that are present in a graph database. An overview visualization simplifies presentation of graph data by depicting only various different node types and graph types. The overview visualization may form the basis for formulating queries including the metadata, which are then promulgated to the graph database. Returned query results may be visualized as an overview or on a detailed node level, promoting insight and formulation of additional queries including node/edge type metadata. The engine may convert graph data query results into tabular form for consumption by relational database analytical tools. According to particular embodiments, an engine of an in-memory database may be particularly suited to perform graph data visualization, querying, and/or tabular conversion tasks.