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.
@article{arxiv.1204.3471,
title = {Cloudpress 2.0: A MapReduce Approach for News Retrieval on the Cloud},
author = {Arockia Anand Raj and T. Mala},
journal= {arXiv preprint arXiv:1204.3471},
year = {2012}
}