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New Technology of Library and Information Service  2015, Vol. 31 Issue (2): 46-54    DOI: 10.11925/infotech.1003-3513.2015.02.07
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Parallel Implementing Bursty Events Detection Using MapReduce
Zhuo Keqiu, Yu Wei, Su Xinning
School of Information Management, Nanjing University, Nanjing 210023, China
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[Objective] In big data environment, this paper aims to accurately and quickly detect bursty events from the text stream. [Methods] Using Kleinberg bursty detection and LDA topic model, the method is extended to MapReduce framework to achieve parallel corpus predisposed, parallel detection of bursty word, parallel filtration of bursty document and parallel extraction of topic. [Results] The results of simulation experiments on the news text stream show that precision reaches 87.50%, recall reaches 77.78%, and F-measure reaches 82.35% with the parallel method to detect bursty events in specific areas. [Limitations] The MapReduce parallel method is difficult to achieve Online and Real-time detection of bursty events with large-scale dynamic text stream. [Conclusions] Compared with the traditional serial detecting method of bursty events, the distributed parallel method not only guarantees the accuracy of detecting results, but also has a good scalability.

Key wordsBursty event detection      MapReduce      Distributed process      LDA topic model     
Received: 04 August 2014      Published: 17 March 2015
:  TP311.1  

Cite this article:

Zhuo Keqiu, Yu Wei, Su Xinning. Parallel Implementing Bursty Events Detection Using MapReduce. New Technology of Library and Information Service, 2015, 31(2): 46-54.

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