Metadata Mapping for Data Service Value Transformations
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Solution Overview
Problem
Existing database systems face challenges in seamlessly integrating and updating data from different data sources due to variations in data representation, requiring manual mapping and transformation of data fields, which is time-consuming and prone to errors.
Innovation Solution
A system and method for value transformations that enable data services to update data objects by creating metadata mappings and transformation objects, allowing users to easily map and transform data fields between different data sources, such as Data.com and Dun & Bradstreet, using Apex code to convert numeric values to textual values and vice versa.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual mapping and transformation of data fields is performed, then data integration between different data sources is achieved, but time consumption and error probability increase
Solution Approach 1:
The system performs preliminary actions by automatically generating metadata mappings between data sources before actual data integration occurs. The mapping framework pre-establishes transformation rules between different data field representations, so when data needs to be integrated, the transformations are already defined and ready to apply, eliminating the need for time-consuming manual mapping at execution time.
Solution Approach 2:
The patent introduces an intermediary metadata layer that acts as a mediator between different data sources. This metadata contains transformation rules and mapping relationships that enable automatic data field transformations without requiring manual intervention. The intermediary metadata framework translates data between different representations (merging, splitting, matching) automatically, reducing both time and errors associated with manual data integration.
2Adaptability or versatility
If custom data objects are created to store non-standard information, then data flexibility and adaptability improve, but system complexity increases
Solution Approach 1:
The patent implements a universal metadata mapping framework that can handle multiple types of data transformations (merging, splitting, matching) through a single unified system. This framework provides multi-functionality by supporting custom data objects with non-standard information while using the same underlying metadata mechanisms, eliminating the need for separate complex mapping code for each transformation type.
Solution Approach 2:
The system manages complexity by allowing parameter changes in data field representations without changing the underlying system structure. The metadata framework accommodates different data formats, representations, and transformations by modifying parameters such as field names, data types, and transformation rules, while maintaining a consistent operational framework. This enables custom data objects to be created with flexible schemas without increasing overall system complexity.
3Adaptability or versatility
If data fields are merged, split, or matched between different data sources, then data compatibility improves, but manual intervention and coding requirements increase
Solution Approach 1:
The system implements self-service by automatically performing data field merging, splitting, and matching operations through the metadata framework. Instead of requiring manual coding for each transformation, the system uses pre-defined metadata rules to automatically execute transformations. The framework self-adapts to different data sources by interpreting metadata mappings and performing the necessary field operations without human intervention.
Solution Approach 2:
The metadata framework serves as an intermediary that automates the complex processes of merging, splitting, and matching data fields between different sources. Rather than requiring direct manual manipulation of data fields, the intermediary metadata layer handles these transformations automatically by applying stored mapping rules, significantly reducing manual effort while maintaining high data compatibility.
Data Source
AI summary
A system receives a user selection of a data object, and outputs the data object to a user device. The system receives a user selection of a data service, and outputs a data service object associated with the data service to the user device. The system receives a user selection of a data field associated with the data object and the data service field, and stores metadata that maps between the data field and the data service field. The system receives a user input, and creates a transformation object that transforms a value associated with the data service field into a value associated with the data field. The system updates an instance of the data object by using the metadata to send request data to the data service, using the metadata to receive response data from the data service, and applying the transformation object.