Due to the significant importance of Big Data analysis, especially in business-related topics such as improving services, finding potential customers, and selecting practical approaches to manage income and expenses, many companies attempt to collaborate with scientists to find how, why, and what they should analysis. In this work, we would like to compare and discuss two different approaches that employed in business analysis topic in Big Data with more consideration on how they utilized Spark. Both studies have investigated Churn Prediction as their case study for their proposed approaches since it is an essential topic in business analysis for companies to recognize a customer intends to leave or stop using their services. Here, we focus on Apache Spark since it has provided several solutions to handle a massive amount of data in recent years efficiently. This feature in Spark makes it one of the most robust candidate tools to upfront with a Big Data problem, particularly time and resource are concerns.
翻译:由于大数据分析的重要性,特别是在改进服务、寻找潜在客户和选择管理收入和支出的实用方法等与商业有关的专题方面,许多公司试图与科学家合作,寻找如何、为什么和应该分析哪些数据。在这项工作中,我们希望比较和讨论大数据中商业分析专题中使用的两种不同方法,更多地考虑它们如何利用Spark。这两项研究都调查了Churn预测作为其拟议方法的案例研究,因为这是公司在商业分析中承认客户打算离开或停止使用其服务的一个重要专题。在这里,我们侧重于Apache Spark,因为它近年来为有效处理大量数据提供了几种解决办法。Spark的这一特征使得它成为应对大数据问题,特别是时间和资源问题的最有力的候选工具之一。