Quality Assurance Issues for Big Data Applications in Supply Chain Management

Kamalendu Pal
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引用次数: 6

Abstract

Heterogeneous data types, widely distributed data sources, huge data volumes, and large-scale business-alliance partners describe typical global supply chain operational environments. Mobile and wireless technologies are putting an extra layer of data source in this technology-enriched supply chain operation. This environment also needs to provide access to data anywhere, anytime to its end-users. This new type of data set originating from the global retail supply chain is commonly known as big data because of its huge volume, resulting from the velocity with which it arrives in the global retail business environment. Such environments empower and necessitate decision makers to act or react quicker to all decision tasks. Academics and practitioners are researching and building the next generation of big-data-based application software systems. This new generation of software applications is based on complex data analysis algorithms (i.e., on data that does not adhere to standard relational data models). The traditional software testing methods are insufficient for big-data-based applications. Testing big-data-based applications is one of the biggest challenges faced by modern software design and development communities because of lack of knowledge on what to test and how much data to test. Big-data-based applications developers have been facing a daunting task in defining the best strategies for structured and unstructured data validation, setting up an optimal test environment, and working with non-relational databases testing approaches. This chapter focuses on big-data-based software testing and quality-assurance-related issues in the context of Hadoop, an open source framework. It includes discussion about several challenges with respect to massively parallel data generation from multiple sources, testing methods for validation of pre-Hadoop processing, software application quality factors, and some of the software testing mechanisms for this new breed of applications
供应链管理中大数据应用的质量保证问题
异构数据类型、广泛分布的数据源、巨大的数据量和大规模的业务联盟伙伴描述了典型的全球供应链操作环境。移动和无线技术为这种技术丰富的供应链运营提供了额外的数据源层。该环境还需要为其最终用户提供随时随地访问数据的能力。这种源于全球零售供应链的新型数据集,由于其到达全球零售商业环境的速度之快,其数量之大,通常被称为大数据。这样的环境使决策者能够更快地对所有决策任务采取行动或作出反应。学者和实践者正在研究和构建下一代基于大数据的应用软件系统。新一代的软件应用程序基于复杂的数据分析算法(即,基于不遵循标准关系数据模型的数据)。传统的软件测试方法对于基于大数据的应用来说是不够的。测试基于大数据的应用程序是现代软件设计和开发社区面临的最大挑战之一,因为缺乏关于测试什么和测试多少数据的知识。基于大数据的应用程序开发人员在定义结构化和非结构化数据验证的最佳策略、设置最佳测试环境以及使用非关系数据库测试方法方面一直面临着艰巨的任务。本章主要讨论基于大数据的软件测试和Hadoop(一个开源框架)环境下的质量保证相关问题。它包括了关于从多个来源大规模并行数据生成的几个挑战的讨论,验证pre-Hadoop处理的测试方法,软件应用程序质量因素,以及这种新型应用程序的一些软件测试机制
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