Big Data Provenance: Challenges, State of the Art and Opportunities.

Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data Pub Date : 2015-10-01 Epub Date: 2015-12-28 DOI:10.1109/BigData.2015.7364047

Jianwu Wang, Daniel Crawl, Shweta Purawat, Mai Nguyen, Ilkay Altintas

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引用次数: 78

Abstract

Ability to track provenance is a key feature of scientific workflows to support data lineage and reproducibility. The challenges that are introduced by the volume, variety and velocity of Big Data, also pose related challenges for provenance and quality of Big Data, defined as veracity. The increasing size and variety of distributed Big Data provenance information bring new technical challenges and opportunities throughout the provenance lifecycle including recording, querying, sharing and utilization. This paper discusses the challenges and opportunities of Big Data provenance related to the veracity of the datasets themselves and the provenance of the analytical processes that analyze these datasets. It also explains our current efforts towards tracking and utilizing Big Data provenance using workflows as a programming model to analyze Big Data.

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大数据来源:挑战、技术现状和机遇。

跟踪来源的能力是支持数据沿袭和再现性的科学工作流的关键特征。大数据的数量、种类和速度带来的挑战，也对大数据的来源和质量(定义为准确性)提出了相关挑战。分布式大数据溯源信息的规模和种类的不断增加，为溯源的记录、查询、共享和利用等整个生命周期带来了新的技术挑战和机遇。本文讨论了大数据来源的挑战和机遇，这些挑战和机遇与数据集本身的真实性以及分析这些数据集的分析过程的来源有关。它还解释了我们目前在跟踪和利用大数据来源方面所做的努力，使用工作流作为编程模型来分析大数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data

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