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Quality Evaluation for Big Data: A Scalable Assessment Approach and First Evaluation Results

机译:大数据质量评估:可扩展评估方法和第一次评估结果

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High-quality data is a prerequisite for most types of analysis provided by software systems. However, since data quality does not come for free, it has to be assessed and managed continuously. The increasing quantity, diversity, and velocity that characterize big data today make these tasks even more challenging. We identified challenges that are specific for big data quality assessments with particular emphasis on their usage in smart ecosystems and make a proposal for a scalable cross-organizational approach that addresses these challenges. We developed an initial prototype to investigate scalability in a multi-node test environment using big data technologies. Based on the observed horizontal scalability behavior, there is an indication that the proposed approach also allows dealing with increasing volumes of heterogeneous data.
机译:高质量数据是软件系统提供的大多数类型分析的先决条件。但是,由于数据质量不免费,因此必须连续评估和管理。表征大数据的数量,多样性和速度的增加,使这些任务更具挑战性。我们确定了对大数据质量评估的具体挑战,特别强调其在智能生态系统中的使用,并提出了一种解决这些挑战的可扩展交叉组织方法。我们开发了一种初始原型,可以使用大数据技术调查多节点测试环境中的可扩展性。基于观察到的水平可扩展性行为,表明所提出的方法还允许处理增加的异构数据量。

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