Semantic Data Object Visualization for Flexible Workflow Navigation
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Solution Overview
Problem
Existing information systems, such as ERP systems, face challenges in effectively navigating and visually presenting large amounts of data objects and their semantic relationships to users, limiting user interaction and analysis within predefined workflows.
Innovation Solution
A system that determines semantically related data objects and presents them graphically, allowing users to interact and alter the visual representation by selecting data objects, which includes multiple graphical elements and semantic relationships, enabling exploration of paths between initial and destination data objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined workflows are used to present data objects, then system structure is maintained, but user flexibility and ad-hoc analysis capabilities are limited
Solution Approach 1:
The system segments the monolithic predefined workflow structure into independent navigable data objects that can be individually selected and explored. Users can navigate through data objects independently rather than being constrained to fixed workflow paths, enabling ad-hoc analysis while maintaining underlying system structure.
Solution Approach 2:
The system transitions from static predefined workflows to dynamic user-driven navigation. The presentation structure adapts based on user selections and semantic relationships, allowing the system to maintain structure while providing flexibility through user-controlled exploration paths.
2Productivity
If multiple data objects are presented in traditional formats, then information completeness is maintained, but user interaction and analysis efficiency are reduced
Solution Approach 1:
The system adds a semantic relationship dimension to traditional data object presentation. By organizing data objects according to their semantic relationships rather than traditional hierarchical or tabular formats, users can analyze information more efficiently through meaningful connections while maintaining information completeness.
Solution Approach 2:
The system creates visual representations (graphical user interface elements) that copy and represent the semantic relationships between data objects. These visual copies enable users to interact with and analyze relationships efficiently without manipulating the actual underlying data structures.
3Loss of information
If semantic relationships are visually presented, then data exploration capability is enhanced, but interface complexity increases
Solution Approach 1:
The system segments the complex web of semantic relationships into individual navigable data objects. Rather than presenting all relationships at once, users can explore relationships through sequential selection of data objects, reducing interface complexity while maintaining comprehensive data exploration capability.
Solution Approach 2:
The system pre-establishes the semantic relationship structure in the background, allowing users to simply navigate and select data objects without needing to understand or construct the relationship framework. This preliminary organization reduces the perceived interface complexity while enabling deep data exploration.
Data Source
AI summary
Various systems and related methods involve navigating, finding, and visually presenting data objects or sets of data objects. In one implementation, a set of data objects is visually presented as a graphical element and one or more semantic relationships between the graphical element and other sets of data objects are visually presented. Furthermore, this includes a method for finding related sets of data objects by presenting different search paths and enabling the user to select a destination set of data objects based on the presented paths. One possible operation associated with this system includes presenting relationships between data objects outside of predefined work flows.