Tenant-Specific Semantic Extensions for Analytic Applications
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Solution Overview
Problem
Existing data analytics environments struggle to accommodate diverse customer requirements for data classification, aggregation, and transformation, particularly in enterprise software applications and cloud environments, leading to time and resource-intensive data extraction and integration processes.
Innovation Solution
An analytic applications environment that uses a shared analytic applications schema with tenant-specific customer schemas, supported by a data pipeline and semantic layer, enabling extensibility and customization through an ETL process and semantic model extensions, allowing customers to populate data warehouses with live data and create customizable software applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a standardized data analytics environment is used, then system stability and ease of deployment are improved, but flexibility to accommodate diverse customer requirements for data classification, aggregation, and transformation deteriorates
Solution Approach 1:
The system segments the data analytics environment into multiple isolated tenant-specific instances, each capable of independent customization. This allows the overall system to maintain stability through standardized core components while providing flexibility through customizable tenant-level configurations for data classification, aggregation, and transformation.
Solution Approach 2:
The system implements dynamic configurability at the tenant level, allowing customers to modify data classification schemes, aggregation rules, and transformation processes according to their specific requirements. This dynamic adaptation occurs within a stable standardized framework, resolving the contradiction between system stability and flexibility.
2Adaptability or versatility
If custom data classification, aggregation, and transformation processes are implemented for each customer, then adaptability to customer requirements is improved, but system complexity and resource requirements worsen
Solution Approach 1:
By segmenting the system into standardized core components and customizable tenant-specific instances, the patent reduces overall system complexity. Each tenant instance handles its own custom requirements independently, preventing complexity from propagating across the entire system while still providing full adaptability to individual customer needs.
Solution Approach 2:
The system employs universal standardized components that can serve multiple tenants simultaneously. These core components handle common data analytics functions efficiently, reducing the need for redundant custom implementations across different customers and thereby lowering overall system complexity while maintaining adaptability.
3Manufacturing precision
If manual data extraction and integration processes are used, then data accuracy and customization precision are improved, but time consumption and resource intensity worsen
Solution Approach 1:
The system performs preliminary automated data extraction, classification, aggregation, and transformation processes before analysis. This preliminary action maintains data accuracy through standardized quality controls while dramatically reducing time consumption compared to manual processes, as the automated pipelines are pre-configured for efficient execution.
Solution Approach 2:
The system implements self-service automated data processing pipelines that extract, classify, aggregate, and transform data without manual intervention. These pipelines maintain data accuracy through built-in validation rules and quality controls while significantly improving time efficiency and reducing resource intensity compared to manual processes.
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for providing support for extensibility and customization in an analytic applications environment. An extract, transform, load (ETL) or other data pipeline or process provided by the analytic applications environment, can operate in accordance with an analytic applications schema and/or a customer schema associated with a customer (tenant), to receive data from the customer's enterprise software application or data environment, for loading into a data warehouse instance. A semantic layer enables the use of custom semantic extensions to extend a semantic model, and provide custom content at a presentation layer. Extension wizards or development environments can guide users in using the custom semantic extensions to extend or customize the semantic model, through a definition of branches and steps, followed by promotion of the extended or customized semantic model to a production environment.