ML Web Data Extraction Adapts to Layout Changes
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Solution Overview
Problem
Existing data collection systems face challenges in efficiently processing large volumes of web data due to irrelevant or redundant information, customized website layouts, and frequent layout changes, which require constant maintenance and updates of parsing code.
Innovation Solution
The implementation of a Machine Learning-enabled data extraction and parsing platform that employs a trained classification model to automatically adapt to changes in website layouts and classify relevant data elements without the need for customer-specific rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional parsing code is used to extract data from websites, then data extraction can be performed, but constant maintenance and updates are required due to customized and frequent layout changes
Solution Approach 1:
The system employs machine learning models that automatically adapt to website layout changes without human intervention. The models learn from collected data and continuously improve their extraction accuracy, enabling the system to serve itself by handling maintenance tasks autonomously.
Solution Approach 2:
Instead of manually updating parsing code when layouts change, the system dynamically adjusts its extraction parameters by retraining machine learning models on new data. This allows the system to adapt to layout changes by changing its internal parameters rather than its structural code.
2Reliability
If traditional parsing code is used to extract data from websites, then data extraction can be performed, but frequent updates are needed due to layout changes
Solution Approach 1:
The system transitions from static parsing code to dynamic machine learning models that can adapt their behavior in real-time. The models continuously learn from new data and adjust their extraction strategies, making the system flexible and responsive to layout changes without requiring code updates.
Solution Approach 2:
The system implements feedback loops where extraction results are continuously evaluated and used to retrain the machine learning models. This feedback mechanism ensures that the models remain accurate even as website layouts evolve, extending the validity period of the extraction system.
3Productivity
If web scrapers are used to collect large amounts of data quickly, then data collection speed is improved, but handling irrelevant and redundant information becomes a challenge
Solution Approach 1:
The system extracts only the relevant features and information from the collected web data using machine learning models. By focusing on extracting meaningful patterns rather than processing all raw data, the system maintains high collection speed while improving data quality and reducing the impact of irrelevant information.
Solution Approach 2:
The machine learning models are pre-trained to recognize and filter relevant information before the main data processing pipeline. This preliminary classification of data relevance occurs during the training phase, enabling the system to quickly identify and prioritize important information during high-speed data collection.
Data Source
AI summary
ADVANCED RESPONSE PROCESSING IN WEB DATA COLLECTION discloses processor-implemented apparatuses, methods, and systems of processing unstructured raw HTML responses collected in the context of a data collection service, the method comprising, in one embodiment, receiving raw unstructured HTML documents and extracting text data with associated meta information that may comprise style and formatting information. In some embodiments data field tags and values may be assigned to the text blocks extracted, classifying the data based on the processing of Machine Learning algorithms. Additionally, blocks of extracted data may be grouped and re-grouped together and presented as a single data point. In another embodiment the system may aggregate and present the text data with the associated meta information in a structured format. In certain embodiments the Machine Learning model may be a model trained on a pre-created training data set labeled manually or in an automatic fashion.