A Novel Combining Method of Dynamic and Static Web Crawler with Parallel Computing

Qingyang Liu,Ramin Yahyapour,Hongjiu Liu,Yanrong Hu

Multimedia Tools and Applications（2024）CCF CSCI 4区

Institute of Informatics | Zhejiang A&F University

Cited 3|Views15

Abstract

Recovering information from a targeted website that undergoes dynamic changes is a complicated undertaking. It necessitates the use of a highly efficient web crawler by search engines. In this study, we merged two web crawlers: Selenium with parallel computing capabilities and Scrapy , to gather electron molecular collision cross-section data from the National Fusion Research Institute ( NFRI ) database. The method effectively combines static and dynamic web crawling. The primary challenges lie in the time-consuming nature of dynamic web crawling using Selenium and that Scrapy ’s limited support for parallel computing within the “download middleware”. Nevertheless, this combined approach proves exceptionally well-suited for the task of data extraction from an online database, which comprises multiple web pages with unchanging URLs when specific keywords are submitted. We applied natural language processing techniques to identify species and dissect reaction formulas into various states. Employing these methodologies, we extracted a total of 76,893 data points pertaining to 112 species. These data pieces offer intricate insights into the processes unfolding within the plasma, all collected within a span of ten minutes. When compared to traditional web crawling methods, our approach boasts a speed advantage of roughly 100 times faster than dynamic web crawlers and exhibits greater flexibility than static web crawlers. In this report, we present the retrieved results, encompassing reaction formulas, reference information, species metadata, and time comparison among various methods.

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Key words

Web crawling,Dynamic,Static,Natural language processing,Parallel computing

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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