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A model architecture for Big Data applications using relational databases

机译:使用关系数据库的大数据应用程序的模型架构

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Effective Big Data applications dynamically handle the retrieval of decisioned results based on stored large datasets efficiently. One effective method of requesting decisioned results, or querying, large datasets is the use of SQL and database management systems such as MySQL. But a problem with using relational databases to store huge datasets is the decisioned result retrieval time, which is often slow largely due to poorly written queries / decision requests. This work presents a model to re-architect Big Data applications in order to efficiently present decisioned results: lowering the volume of data being handled by the application itself, and significantly decreasing response wait times while allowing the flexibility and permanence of a standard relational SQL database, supplying optimal user satisfaction in today's Data Analytics world. In this paper we review a Big Data case study in the telecommunications field and use it to experimentally demonstrate the effectiveness of our approach.
机译:有效的大数据应用程序可根据存储的大型数据集有效地动态处理决策结果的检索。请求决策结果或查询大型数据集的一种有效方法是使用SQL和数据库管理系统(例如MySQL)。但是,使用关系数据库存储大量数据集的问题是决策结果的检索时间,由于查询/决策要求不佳,这通常会很慢。这项工作提出了一个用于重新架构大数据应用程序的模型,以便有效地呈现决策结果:减少应用程序本身处理的数据量,并显着减少响应等待时间,同时允许标准关系SQL数据库的灵活性和持久性,在当今的数据分析世界中提供最佳的用户满意度。在本文中,我们回顾了电信领域的大数据案例研究,并用它来实验性地证明了我们方法的有效性。

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