Abstract
Currently available web news retrieval systems face a number of problems in that web-based news retrieval requires the ability to quickly and accurately process and update a very large amount of data which are constantly being updated. In this paper, we present the development of an intelligent distributed web news retrieval system the goal of which is to accurately retrieve and organize the web news information. It includes: a novel optimized crawler algorithm whose fetching-speed is several times faster than that of the traditional crawler; a keen tag based extraction algorithm which can extract the data rich content with minimal manual effort and which also allows data to be classified as important or not important so that the crawler can revisit and update important data; a modified MapReduce improved by estimating the execution time of each subtask, which is proven to be able to reduce the number of the unusual tasks and shorten the whole job execution time.
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