Architecture

• Data Collection: Gather raw data from various sources, including text, images, audio, etc.

• Data Cleaning: Remove duplicates, irrelevant or erroneous data, and handle missing or anomalous values.

• Data Annotation: Annotate the data as needed to ensure accuracy and consistency.

Initial Data Filtering

• Data Preprocessing: Perform preprocessing steps such as normalization, standardization, or tokenization.

• Data Classification: Classify the data based on features and labels to perform initial filtering and identify potentially useful datasets.

Data Evaluation Layer

• Define Evaluation Metrics: Establish metrics for assessing data quality, such as accuracy, completeness, consistency, and relevance.

• Develop Evaluation Models: Create or select appropriate evaluation models, which may include rule-based systems, statistical methods, or machine learning models.

  • Rule-Based Evaluation: Set rules and standards to assess data quality.

  • Statistical Methods: Use statistical methods to detect anomalies and biases in the data.

  • Machine Learning Models: Train machine learning models to evaluate data quality.

• Execute Evaluation: Apply the evaluation models to the data, generating quality reports and scores.

• Feedback Loop: Adjust data collection and processing workflows based on evaluation results to improve data quality.

Data Selection and Filtering

• Filter Based on Evaluation Results: Select high-quality datasets based on evaluation outcomes.

• Data Integration: Integrate the filtered datasets into the training dataset, ensuring consistency and high quality.