大数据分析是指对规模巨大的数据进行分析。大数据可以概括为5个V, 数据量大(Volume)、速度快(Velocity)、类型多(Variety)、价值(Value)、真实性(Veracity)。

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大数据分析的一个关键挑战是如何收集大量(标记)数据。众包旨在通过聚合和估算来自广泛的客户/用户的高质量数据(如文本的情感标签)来解决这一挑战。现有的众包研究集中于设计新的方法来提高来自不可靠/嘈杂客户端的聚合数据质量。然而,迄今为止,这种众包系统的安全方面仍未得到充分的探索。我们的目标是在这项工作中填补这一缺口。具体来说,我们表明众包很容易受到数据中毒攻击,即恶意客户端提供精心制作的数据来破坏聚合数据。我们将我们所提议的数据中毒攻击规划为一个优化问题,使聚合数据的错误最大化。我们在一个合成的和两个真实的基准数据集上的评估结果表明,所提出的攻击可以显著地增加聚合数据的估计误差。我们还提出了两种防御来减少恶意客户端的影响。我们的实证结果表明,所提出的防御方法可以显著降低数据中毒攻击的估计误差。

https://www.zhuanzhi.ai/paper/d25992f7a7df3ee1468f244f05a8ba03

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Massive data from software repositories and collaboration tools are widely used to study social aspects in software development. One question that several recent works have addressed is how a software project's size and structure influence team productivity, a question famously considered in Brooks' law. Recent studies using massive repository data suggest that developers in larger teams tend to be less productive than smaller teams. Despite using similar methods and data, other studies argue for a positive linear or even super-linear relationship between team size and productivity, thus contesting the view of software economics that software projects are diseconomies of scale. In our work, we study challenges that can explain the disagreement between recent studies of developer productivity in massive repository data. We further provide, to the best of our knowledge, the largest, curated corpus of GitHub projects tailored to investigate the influence of team size and collaboration patterns on individual and collective productivity. Our work contributes to the ongoing discussion on the choice of productivity metrics in the operationalisation of hypotheses about determinants of successful software projects. It further highlights general pitfalls in big data analysis and shows that the use of bigger data sets does not automatically lead to more reliable insights.

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