Autonomous Crawler Frequency Control via PID and LSTM Prediction
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Solution Overview
Problem
Current internet crawling systems face inefficiencies in determining optimal crawl frequencies, leading to high computational costs and a risk of missing content changes, as they either produce excessive redundant data or fail to capture changes due to infrequent crawling.
Innovation Solution
The system dynamically determines crawl frequency by computing proportional, integral, and derivative contributions, using rate of change and displacement measurements, and implements a recursive neural network with long short-term memory units to predict future data changes, optimizing wait times between crawls and minimizing redundant data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high frequency crawling is performed, then content change detection accuracy is improved, but computational cost increases
Solution Approach 1:
The system dynamically adjusts crawl frequency based on real-time data change detection. When changes are detected, the crawler increases frequency to capture updates; when no changes occur, it reduces frequency to save computational resources. This dynamic adaptation resolves the contradiction by making crawl frequency variable rather than fixed.
Solution Approach 2:
The system changes the parameter of crawl frequency based on detected data dynamics. By monitoring data change indicators and adjusting the crawl interval parameter accordingly, the system achieves high detection accuracy when needed while reducing computational cost during stable periods.
2Measurement precision
If high frequency crawling is performed, then content change detection accuracy is improved, but redundant data storage increases
Solution Approach 1:
The system discards redundant crawl data by comparing current crawl results with previously stored data. When no changes are detected, the system avoids storing duplicate information, thereby reducing data storage requirements while maintaining detection accuracy through selective data retention.
Solution Approach 2:
The system extracts only the essential change information from crawl data rather than storing complete duplicate datasets. By taking out only the meaningful changes and discarding redundant identical data, the system reduces storage requirements while preserving detection capability.
3Use of energy by moving object
If low frequency crawling is performed, then computational cost is reduced, but content change detection accuracy deteriorates
Solution Approach 1:
The system uses feedback from data change detection to control crawl frequency. When changes are detected in the monitored data, the system receives feedback to increase crawl frequency, ensuring accurate detection. This feedback loop prevents accuracy deterioration while maintaining computational efficiency during stable periods.
Solution Approach 2:
The system performs preliminary data change detection before executing full crawls. By checking for changes in advance using lightweight monitoring mechanisms, the system can prepare appropriate crawl frequency in advance, avoiding both unnecessary high-frequency crawls and missed changes.
4Measurement precision
If crawl frequency is increased, then content change detection accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The system implements periodic crawling with variable intervals rather than continuous or fixed-frequency crawling. The periodic action adapts its period based on data dynamics, performing frequent crawls only when changes are detected and using longer intervals when data is stable, thereby reducing overall network bandwidth consumption while maintaining detection accuracy.
Data Source
AI summary
The system which dynamically determines crawl frequency. Crawl frequency is computed by determining the proportional integral derivative (PID) contribution of certain data to the system. Alternatively, the system determines the next time to crawl based on both the rate of change and displacement of data. Maximum and minimum wait times between crawls are taken into account for the computation. In another embodiment, crawl frequency is calculated based on the rate of change. The system determines the next time to crawl based on the rate of change of data within set parameters, such as maximum and minimum wait times between crawls. Alternatively, the system implements a recurrent neural network (RNN) using long short-term memory (LSTM) units for a future data prediction. With this information, the system determines the next time to crawl.