Object Relationship Templates for Dynamic Data Extraction
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Solution Overview
Problem
Enterprises face challenges in extracting meaningful subsets of data from large datasets for business analytics and reporting, requiring significant effort and repeated modeling processes to adapt to changing business needs.
Innovation Solution
The use of object relationship templates in an enterprise cloud platform allows for the creation, editing, and reuse of data extraction flows, ensuring consistency, accuracy, and efficiency in data extraction, enabling non-analyst users to build data extract graphs without detailed knowledge of underlying data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analysts manually model data repeatedly to extract meaningful subsets, then data extraction accuracy can be maintained, but significant time and effort are expended
Solution Approach 1:
The system performs preliminary actions by automatically discovering and modeling relationships between data objects before extraction is needed. The relationship modeling component pre-establishes connections between objects based on schema information and data patterns, so when extraction is required, the work is already done and can be reused.
Solution Approach 2:
The system creates copies of relationship models that can be reused across multiple extraction operations. Once relationships are discovered and modeled, they are stored as reusable templates that can be copied and applied to different data extraction scenarios without repeating the modeling process.
2Productivity
If traditional data extraction methods are used, then data can be extracted for analysis, but the process requires significant analyst effort and cannot quickly adapt to changing business needs
Solution Approach 1:
The system implements dynamics by making the data extraction process adaptable and flexible. The relationship modeling component continuously discovers new relationships based on changing data patterns, and the system can dynamically adjust extraction models to reflect new business requirements without manual reconfiguration.
Solution Approach 2:
The system incorporates feedback mechanisms where extraction results and usage patterns inform subsequent relationship discoveries. The system learns from how data is extracted and used, refining its relationship models to better serve evolving business needs and improving extraction efficiency over time.
3Reliability
If complex data modeling is performed to handle multi-tenant database systems, then data integrity is maintained, but the complexity of the extraction process increases
Solution Approach 1:
The system introduces an intermediary relationship modeling component that sits between the complex multi-tenant database system and the extraction process. This intermediary automatically manages the complexity of relationships between objects across multiple tenants, translating complex database structures into manageable extraction models while preserving data integrity.
Solution Approach 2:
The relationship modeling component performs self-service by automatically discovering and modeling relationships without requiring manual intervention. The system autonomously analyzes schema information and data patterns to establish relationships, reducing the complexity burden on users while maintaining data integrity through automated relationship management.
Data Source
AI summary
Methods, systems, apparatus, and machine-readable media facilitate a system for data extraction using object relationship templates. In an enterprise cloud computing environment, a system for data extraction using object relationship templates is implemented to dynamically generate data extraction flows from one or more enterprise data sources quickly and accurately in response to changing business needs. Object relationship templates representing all of a portion of a data extraction flow are created and stored for reuse either alone or in combination with other data extraction flows and other templates to create new data extraction flows. Corresponding methods, systems, apparatus, and machine-readable media for data extraction using object relationship templates can be implemented in servers supporting the enterprise cloud computing environment.