17964084. ONE-HOT ENCODER USING LAZY EVALUATION OF RELATIONAL STATEMENTS simplified abstract (Oracle International Corporation)
Contents
- 1 ONE-HOT ENCODER USING LAZY EVALUATION OF RELATIONAL STATEMENTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ONE-HOT ENCODER USING LAZY EVALUATION OF RELATIONAL STATEMENTS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
ONE-HOT ENCODER USING LAZY EVALUATION OF RELATIONAL STATEMENTS
Organization Name
Oracle International Corporation
Inventor(s)
FELIX Schmidt of Baden-Dattwil (CH)
MATTEO Casserini of Zurich (CH)
ONE-HOT ENCODER USING LAZY EVALUATION OF RELATIONAL STATEMENTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17964084 titled 'ONE-HOT ENCODER USING LAZY EVALUATION OF RELATIONAL STATEMENTS
Simplified Explanation
The abstract describes a method and storage media for training and implementing a one-hot encoder, which involves extracting unique categories, associating them with unique indices, and generating one-hot encodings. The process includes executing relational statements using a query optimization engine and encoding categorical features based on the encoder state.
- Explanation of the patent:
- During training, unique categories are extracted and associated with unique indices.
- One-hot encodings are generated for each unique category.
- Relational statements are executed using a query optimization engine.
- Execution of statements is postponed until needed, with optimizations implemented.
- Categorical features in a second training data set are encoded based on the encoder state.
Potential Applications
The technology can be applied in various fields such as machine learning, data analysis, and natural language processing.
Problems Solved
1. Efficient encoding of categorical features. 2. Optimization of query execution for better performance.
Benefits
1. Improved data processing efficiency. 2. Enhanced performance in training and implementing one-hot encoders.
Potential Commercial Applications
Optimizing data processing in industries such as e-commerce, finance, and healthcare.
Possible Prior Art
Prior art may include existing methods for encoding categorical features and optimizing query execution in data processing systems.
Unanswered Questions
How does the technology handle large datasets efficiently?
The efficiency of the technology with large datasets may depend on the scalability and optimization techniques used in the implementation.
What are the potential limitations of the query optimization engine?
The limitations of the query optimization engine, such as handling complex relational statements or specific data types, could impact the overall performance of the technology.
Original Abstract Submitted
A method and one or more non-transitory storage media are provided to train and implement a one-hot encoder. During a training phase, computation of an encoder state is performed by executing a set of relational statements to extract unique categories in a first training data set, associate each unique category with a unique index, and generate a one-hot encoding for each unique category. The set of relational statements are executed by a query optimization engine. Execution of the set of relational statements is postponed until a result of each relational statement is needed, and the query optimization engine implements one or more optimizations when executing the set of relational statements. During an encoding phase, a set of categorical features in a second training data set are encoded based on the encoder state to form a set of encoded categorical features.
