Developing object ontologies and data usage models using machine learning
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Publication Number
US-12153615-B2
Publication Date
2024-11-26
Expiration Date
Abstract
An enterprise ontology, an application data usage model, and/or cross-application data dependencies may be developed using artificial intelligence. Using pattern recognition and/or information extraction techniques, the artificial intelligence may analyze application source code to identify common DDL or SQL statements to formulate an ontology and/or a usage model for the application. A plurality of application ontologies and/or data usage models may be used to build a semantic hub. The semantic hub may be analyzed to identify data redundancies, data use frequency, potential data quality challenges, and/or data dependencies between applications to produce a data abstraction model that allows legacy applications to communicate with one or more data stores.
Core Innovation
The invention generates, by a computing device, a first application data usage model illustrating a relationship between a first application and one or more data stores, based on a link between one or more data statements and database object metadata. The first application data usage model is used as an input for subsequent semantic processing.
The invention then generates, based on the first application data usage model and at least one second application data usage model, a semantic hub by combining application usage models. The semantic hub is analyzed to support cross-application understanding, including identifying data redundancy, data use frequency, data quality challenge, and data dependency across applications.
Based on the semantic hub, the invention generates a data abstraction model configured to transform data exchanges between a second application and at least one data store of the one or more data stores. The data abstraction model enables legacy application communication with heterogeneous data stores by providing a unified semantic-based layer for data exchange transformation, and the data abstraction model may include microservices as part of the transformation configuration.
The semantic hub can also be displayed via a dashboard, and one or more data lineage maps can be displayed on a user device based on analysis of the semantic hub. In some implementations, a machine learning model analyzes the semantic hub and identifies at least one of data redundancy, data use frequency, data quality challenge, or data dependency, including variants that identify such issues by comparing the first application data usage model with the at least one second application data usage model.
Claims Coverage
The document includes three independent claim types, covering the same core sequence of generating a first application data usage model from links between data statements and database object metadata, generating a semantic hub from application usage models, and generating a data abstraction model that transforms data exchanges. The independent claim families cover 3 core inventive features, with dependent refinements for analysis, visualization, microservices, and data-statement types.
Application data usage model generation from data-statement and database-metadata links
Generating, by a computing device, a first application data usage model illustrating a relationship between a first application and one or more data stores, wherein the generation is based on a link between one or more data statements and database object metadata.
Semantic hub generation from multiple application data usage models
Generating, based on the first application data usage model and at least one second application data usage model, a semantic hub.
Semantic-hub-based data abstraction model for transforming data exchanges
Generating, based on the semantic hub, a data abstraction model configured to transform data exchanges between a second application and at least one data store of the one or more data stores.
Computing device implementation of semantic-hub-driven data exchange transformation
A computing device comprising one or more processors and memory storing instructions that, when executed, cause the computing device to perform the generation of the first application data usage model, the semantic hub, and the data abstraction model configured to transform data exchanges between a second application and at least one data store.
Non-transitory instructions medium for generating semantic hub and abstraction model
A non-transitory computer-readable medium comprising instructions that, when executed, cause a computing device to generate the first application data usage model, generate the semantic hub, and generate the data abstraction model configured to transform data exchanges between a second application and at least one data store.
All independent claims cover a computing-driven pipeline: generating application data usage models from links between one or more data statements and database object metadata, combining multiple usage models to generate a semantic hub, and generating a data abstraction model based on the semantic hub to transform data exchanges between a second application and one or more data stores.
Stated Advantages
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