Versioning Data During Migration Processes in Cloud Environment

Roman Ceresnák, K. Matiaško
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Abstract

Nowadays, big data influences many aspects of human life. They help in medicine with diagnosing different illnesses, in traffic with watching of traffic accidents, and of course, they have a crucial role in supporting decisions. It is appropriate to test another database, respectively, another database type, in every operation's unsatisfactory performance by using a set database. A transformation process is needed in this case. Big Data entering this database has a different structure and size, which influences the set transformation process's time difficulty. The transformation process changes the data structure, from relational to nonrelational, respectively nonrelational to a relational database, making it possible to stop the process or an error that can end up with an incomplete change of a data structure and the data this process must have been repeated. “Version” system we created in this paper is, in the case of incomplete data change, respectively failure of transformation process during the transformation of a relational database to a nonrelational or nonrelational database to relational, capable of continuing from the error point of the previous approach, and so it can erase necessity to perform whole transformation process from the very first beginning.
云环境下迁移过程中的数据版本控制
如今,大数据影响着人类生活的方方面面。它们在医学上帮助诊断不同的疾病,在交通上帮助观察交通事故,当然,它们在支持决策方面也起着至关重要的作用。在每个操作的性能不理想的情况下,使用一个集数据库分别测试另一个数据库,另一个数据库类型是合适的。在这种情况下,需要一个转换过程。进入该数据库的大数据具有不同的结构和大小,这影响了集合转换过程的时间难度。转换过程更改数据结构,从关系数据库更改为非关系数据库,从非关系数据库更改为关系数据库,从而有可能停止该过程或出现错误,从而导致数据结构的不完全更改和该过程必须重复的数据。本文所创建的“版本”系统,在数据变更不完全的情况下,分别在关系型数据库到非关系型数据库或非关系型数据库到关系型数据库的转换过程中出现了转换过程的失败,能够从之前方法的错误点继续下去,从而消除了从头开始执行整个转换过程的必要性。
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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