Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system and method to analyze product information by receiving product information from a third party server. The system filters the product information for review information, generates a score for the review information, and generates a notification based on the score.