Web Crawler for Humanitarian Crisis Analysis
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Solution Overview
Problem
Analyzing humanitarian crises using real-time data from smartphones and portable devices is challenging due to the unstructured, informal, and personalized nature of the data collected, which hinders effective decision-making and response strategies.
Innovation Solution
A computer-implemented method using a trained learning model to crawl networks, identify secondary-source documents, extract information, classify documents into categories related to the crisis, and receive feedback for model retraining, facilitating efficient data analysis and situational awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and annotation methods are used, then data accuracy can be ensured through human review, but the time required for data processing becomes excessively long
Solution Approach 1:
The patent replaces manual mechanical data collection and annotation processes with automated web crawling systems and machine learning models. The crawler automatically navigates websites, extracts information, and the ML models automatically classify and annotate data, eliminating the need for human reviewers while maintaining or improving accuracy through systematic automated processing.
Solution Approach 2:
The system enables self-service data processing where the web crawler autonomously navigates and collects data from multiple sources, and machine learning models automatically annotate and classify the collected information without requiring human intervention at each step. The system serves itself by using its own outputs (collected data) to train and improve its own components.
2Measurement precision
If comprehensive data from multiple sources is collected to improve analysis accuracy, then the quantity and quality of information increase, but the complexity of data management and processing increases
Solution Approach 1:
The patent segments the complex data collection and processing task into distinct modular components: a web crawler module for data collection, a machine learning model for classification, and an annotation system. Each component handles a specific aspect of the process, making the overall system more manageable and easier to maintain while processing comprehensive data from multiple sources.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw collected data and final crisis analysis results. These ML models automatically process, classify, and structure the unstructured data from multiple sources, serving as a mediator that transforms heterogeneous data into standardized, analyzable information without requiring manual intervention.
3Speed
If real-time data processing is implemented to enable faster response, then response time improves, but the computational resources and system complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on historical crisis data before actual crisis events occur. The web crawler is pre-configured with target websites and crawling parameters. When a crisis event occurs, the pre-prepared system can immediately begin processing real-time data without requiring setup or initialization, enabling faster response while the complexity is managed through prior preparation.
Data Source
AI summary
A network is crawled using a trained learning model to identify a set of secondary-source documents related to an event. A hub page from the set of secondary-source documents is identified that includes a link predicted to link to a new relevant secondary-source document. The new document is added to the set of secondary-source documents. Information is extracted from the set of secondary-source documents. Feedback is received indicative of a relevancy level for the extracted information as applied to the event. Each document is classified into one or more categories related to the event, based on the extracted information and the received feedback information. A learning model is trained based on the received feedback.