维基百科作者影响力计算框架的实施与评估
Implementation and Evaluation of a Framework to calculate Impact Measures for Wikipedia Authors
摘要：
维基百科是一个开放的合作网站，任何人都可以编辑，甚至匿名编辑，因此成为恶意修改的受害者。因此，通过基于编辑历史计算影响力来对维基百科作者进行排名，有助于识别声誉用户或有害活动，如破坏行为\cite{Adler:2008:MAC:1822258.1822279}。但是，在一个系统上处理数百万个编辑可能需要很长时间。作者实现了一个开源框架，以分布式方式（MapReduce）计算这种排名，并评估其在各种大小的数据集上的性能。\citeauthor{Adler:2008:MAC:1822258.1822279}的贡献措施的重新实现证明了其可扩展性和可用性，以及处理庞大的数据集和可能的解决方案的问题。结果表明，水平缩放可以减少总的处理时间。
英文摘要：
Wikipedia, an open collaborative website, can be edited by anyone, even anonymously, thus becoming victim to ill-intentioned changes. Therefore, ranking Wikipedia authors by calculating impact measures based on the edit history can help to identify reputational users or harmful activity such as vandalism \cite{Adler:2008:MAC:1822258.1822279}. However, processing millions of edits on one system can take a long time. The author implements an open source framework to calculate such rankings in a distributed way (MapReduce) and evaluates its performance on various sized datasets. A reimplementation of the contribution measures by \citeauthor{Adler:2008:MAC:1822258.1822279} demonstrates its extensibility and usability, as well as problems of handling huge datasets and their possible resolutions. The results put different performance optimizations into perspective and show that horizontal scaling can decrease the total processing time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
