Cloudpress 2.0: A MapReduce Approach for News Retrieval on the Cloud

TL;DR

Cloudpress 2.0 leverages MapReduce and cloud computing to enable scalable, robust, and efficient news retrieval, including indexing, summarization, and visualization, handling large-scale news data effectively.

Contribution

It introduces a novel cloud-based news retrieval system that integrates MapReduce, distributed storage, Lucene indexing, and 3D visualization for improved performance and user experience.

Findings

Achieves scalable news processing on the cloud

Provides faster retrieval with Lucene-based indexing

Includes innovative 3D visualization of news articles

Abstract

In this era of the Internet, the amount of news articles added every minute of everyday is humongous. As a result of this explosive amount of news articles, news retrieval systems are required to process the news articles frequently and intensively. The news retrieval systems that are in-use today are not capable of coping up with these data-intensive computations. Cloudpress 2.0 presented here, is designed and implemented to be scalable, robust and fault tolerant. It is designed in such a way that, all the processes involved in news retrieval such as fetching, pre-processing, indexing, storing and summarizing, exploit MapReduce paradigm and use the power of the Cloud computing. It uses novel approaches for parallel processing, for storing the news articles in a distributed database and for visualizing them as a 3D visual. It uses Lucene-based indexing for efficient and faster retrieval. It also includes a novel query expansion feature for searching the news articles. Cloudpress 2.0 also allows on-the-fly, extractive summarization of news articles based on the input query.