Dynamic Semantic Item Identification in Business Analytics
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Solution Overview
Problem
Business enterprises face high costs and inefficiencies in maintaining and updating data analysis systems, as they require frequent hiring of consultants and integrators to modify reports and add new features, due to static data and security constraints.
Innovation Solution
A top-down business analysis architecture that dynamically identifies and generates analysis interfaces based on available data and user permissions, allowing for out-of-the-box functionality and adaptive growth without the need for additional consultants, by prioritizing content over data availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a static data model with fixed security constraints is used, then system stability is maintained, but adaptability to changing data availability and user roles deteriorates
Solution Approach 1:
The patent implements dynamic identification of supported semantic items by evaluating data availability and user permissions at runtime. The system transitions from static security constraints to dynamic assessment, where the set of supported items changes based on current data availability and user role, resolving the contradiction between stability and adaptability
Solution Approach 2:
The system changes parameters (data availability status, user permission levels) to dynamically determine which semantic items are supported. By monitoring and responding to parameter changes in the environment, the system maintains stability while adapting to new conditions without requiring structural modifications
2Adaptability or versatility
If consultants and integrators are hired to modify reports and add features, then functionality and adaptability are improved, but cost and complexity increase
Solution Approach 1:
The system performs self-service by automatically identifying supported semantic items based on available data and user permissions. This eliminates the need for external consultants to manually configure and update reports, as the system adapts autonomously to changing conditions, reducing both cost and organizational complexity
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring data availability and user permission changes, then automatically adjusting the set of supported semantic items. This closed-loop approach enables the system to adapt functionality without human intervention, resolving the contradiction between adaptability and complexity
3Ease of operation
If a top-down content-first approach is used, then ease of operation and user experience are improved, but the system must handle data unavailability and dynamic changes
Solution Approach 1:
The patent introduces an intermediary layer that sits between the content model and the data source. This intermediary dynamically evaluates data availability and user permissions, translating complex backend conditions into simple, consistent user interfaces. Users experience ease of operation while the intermediary handles the complexity of dynamic data availability and permission management
Data Source
AI summary
Systems and methods of dynamically identifying supported items in an application are described. In one example, an analytics engine receives an indication of available client data, a user's enterprise role, and/or a user's security level. The analytics engine identifies a first subset of supported semantic items (e.g., business topics, business topics, measures, etc.) and a second subset of unsupported semantic items. For example, a semantic item may be supported if corresponding client data is available for analysis and the user's role/security level enable access to the client data. The analytics engine may send data including the supported semantic items and excluding the unsupported semantic items to an application.