Trustworthiness of Big Data
Trustworthiness of Big Data
复制标题
大数据的可信度
DOI：
10.5120/13892-1835
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
A. Mittal
中科院分区：
文献类型：
--
作者：
A. Mittal
大数据指的是对存储、搜索、共享、可视化和分析等测试具有挑战性的大型数据集。信息以不稳定的速度出现，从不同的领域以多种形式进入组织。由于技术变革、基础设施（云上的DB/ETL）和大数据，传统的DW测试方法已经不够用了。大数据验证不仅仅是验证不同之处；它还涉及到对已经拥有的新集成组件的验证。由于数据质量差仍然是一个主要且呈指数增长的问题，因此存在独特的测试前景。这是一个数字世界，它导致了数据量（数据量）、速度（数据输入和输出的速度）和数据种类（数据类型和来源的范围）的大量增加。因此，对现实数据集、数据准确性、一致性和数据质量的关注现在是一个关键问题。本文试图探讨大数据应用中的测试挑战，并概述了一种测试策略，以验证高容量、高速度和多样化的信息。大数据，数据仓库，测试。
Big data refers to large datasets that are challenging to store, search, share, visualize, and analyze and so the Testing. Information is emerging at volatile rate, coming into organization from diverse areas and in numerous formats. Traditional DW testing approach is inadequate due to Technology Changes, Infrastructure (DB/ETL on Cloud) and Big Data. Big Data validation is not only around validation of just what is different; it’s also about validation of new integrated components to what you already have. There is unique testing prospects exists as poor data quality is still a major and exponentially growing problem. It’s a digital world, which is causing massive increases in the volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources) of data. As a result, concern for realistic data sets, data accuracy, consistency and data quality is now a critical issue. The paper tries to explore testing challenges in Big Data adoption and outline a testing strategy to validate high volume, velocity and variety of information. General Terms Big Data, Data warehousing, Testing.