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High Performance Risk Aggregation: Addressing the Data Processing Challenge the Hadoop MapReduce Way
Year Of Publication: 2013
Month Of Publication: January
Pages: 6
Download Count: 5
View Count: 1852
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 2-24-2013
Publisher: Administrator
Monte Carlo simulations employed for the analysis of portfolios of catastrophic risk can benefit from platforms that process large volumes of data by exploiting parallelism. To achieve this an algorithm for the analysis of aggregate risk is proposed and implemented using the MapReduce model on the Apache Hadoop framework. An evaluation of the performance of the algorithm indicates that the Hadoop MapReduce model offers a feasible platform for processing large data. An aggregate simulation of 100,000 trials with 1000 catastrophic events per trial on a typical exposure set and contract structure is performed on multiple worker nodes in about 6 minutes. The result indicates the scope and feasibility of MapReduce for tackling the data problem in the analysis of aggregate risk.
This document was published in Proceedings of the 4th ACM Workshop on Scientific Cloud Computing (2013), 53-60.
Varghese, Blesson Sign in to follow this author
Rau-Chaplin, Andrew Sign in to follow this author
Yao, Zhimin Sign in to follow this author
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