Oracle international corporation (20240126789). MODEL MINING AND RECOMMENDATION ENGINE WITH SIMULATION INTERFACES simplified abstract
Contents
- 1 MODEL MINING AND RECOMMENDATION ENGINE WITH SIMULATION INTERFACES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODEL MINING AND RECOMMENDATION ENGINE WITH SIMULATION INTERFACES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology ensure the security and privacy of the data being processed and reviewed?
- 1.11 What are the scalability limitations of this technology when dealing with large datasets and complex models?
- 1.12 Original Abstract Submitted
MODEL MINING AND RECOMMENDATION ENGINE WITH SIMULATION INTERFACES
Organization Name
oracle international corporation
Inventor(s)
Hari Bhaskar Sankaranarayanan of Bengaluru (IN)
Rajarshi Bhose of Bangalore (IN)
MODEL MINING AND RECOMMENDATION ENGINE WITH SIMULATION INTERFACES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240126789 titled 'MODEL MINING AND RECOMMENDATION ENGINE WITH SIMULATION INTERFACES
Simplified Explanation
The present embodiments relate to data processing model recommendation and review of a portion of data using a recommended model. A model catalog executing on a cloud infrastructure (CI) system can parse data from an obtained dataset identifying aspects of the dataset. The parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. Review output data can be generated using the dataset and any of the recommended models. The review output data resulting from the recommended model can be provided to the client for the client to either accept or reject the model.
- Data processing model recommendation and review
- Model catalog on a cloud infrastructure system
- Parsing data from a dataset to identify aspects
- Comparison with potential models in a domain ontology store
- Generating review output data using recommended models
- Client acceptance or rejection of the recommended model
Potential Applications
This technology could be applied in various industries such as finance, healthcare, marketing, and more for data analysis and model recommendation purposes.
Problems Solved
This technology helps in automating the process of model recommendation and review, saving time and effort for data analysts and researchers.
Benefits
- Improved efficiency in data processing and model selection - Enhanced decision-making based on recommended models - Streamlined workflow for data analysis tasks
Potential Commercial Applications
"Data Processing Model Recommendation and Review" technology could be utilized in industries such as finance, healthcare, and marketing for optimizing data analysis processes and improving decision-making based on recommended models.
Possible Prior Art
One possible prior art for this technology could be the use of machine learning algorithms for model recommendation and data analysis tasks in various industries.
Unanswered Questions
How does this technology ensure the security and privacy of the data being processed and reviewed?
The article does not provide details on the security measures implemented to protect the data during processing and review.
What are the scalability limitations of this technology when dealing with large datasets and complex models?
The article does not address the potential scalability challenges that may arise when processing large datasets and complex models.
Original Abstract Submitted
the present embodiments relate to data processing model recommendation and review of a portion of data using a recommended model. a model catalog executing on a cloud infrastructure (ci) system can parse data from an obtained dataset identifying aspects of the dataset. the parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. review output data can be generated using the dataset and any of the recommended models. the review output data resulting from the recommended model can be provided to the client for the client to either accept or reject the model.
