18100158. GENERATING RANDOMIZED TABULAR DATA simplified abstract (Saudi Arabian Oil Company)
Contents
GENERATING RANDOMIZED TABULAR DATA
Organization Name
Inventor(s)
Badr Al-dhalaan of Dhahran (SA)
GENERATING RANDOMIZED TABULAR DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18100158 titled 'GENERATING RANDOMIZED TABULAR DATA
Simplified Explanation: The patent application describes a computer-implemented method for generating test data based on received configurations and dependencies that define ranges of values and relationships between fields in a test dataset.
Key Features and Innovation:
- Computer-implemented method for generating test data
- Configurations define ranges of values for each field
- Dependencies restrict test values based on relationships between fields
- Conditional dependency imposes restrictions based on pre-determined conditions
- Relational dependency enforces direct relationships between fields
Potential Applications: This technology can be used in software development, quality assurance testing, data analysis, and machine learning applications.
Problems Solved:
- Efficient generation of test data
- Ensuring data integrity and accuracy
- Handling complex dependencies between fields in a dataset
Benefits:
- Improved testing accuracy
- Time-saving in test data generation
- Enhanced data quality and reliability
Commercial Applications: The technology can be applied in software development companies, data analytics firms, and industries requiring rigorous testing protocols.
Prior Art: Researchers can explore existing patents related to test data generation, data dependencies, and software testing methodologies.
Frequently Updated Research: Stay updated on advancements in data generation algorithms, machine learning techniques for test data creation, and software testing automation tools.
Questions about Test Data Generation: 1. How does this technology improve the efficiency of test data generation? 2. What are the potential challenges in implementing complex dependencies in test data generation algorithms?
Original Abstract Submitted
Systems and methods include a computer-implemented method for generating test data. Configurations are received that define, for each field of a test dataset to be generated, ranges of values for which test values are to be generated for each field. Dependencies are received that define, for each field of the test dataset, relationships with one or more fields and that restrict the test values to be generated for each field of the test dataset. A conditional dependency is received that imposes restrictions on a value range of field X when field Y meets a pre-determined condition. A relational dependency is received that enforces a direct relationship between two or more fields. The relational dependency is in a form of an inequality between field X and field Z. A test data set is generated using the fields, configurations, and dependencies, including generating random test data for each of the configurations.
