With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past decades and have become massively popular, especially in industries. It is becoming increasingly evident that effective big data analysis is key to solving artificial intelligence problems. Thus, a multi-algorithm library was implemented in the Spark framework, called MLlib. While this library supports multiple machine learning algorithms, there is still scope to use the Spark setup efficiently for highly time-intensive and computationally expensive procedures like deep learning. In this paper, we propose a novel framework that combines the distributive computational abilities of Apache Spark and the advanced machine learning architecture of a deep multi-layer perceptron (MLP), using the popular concept of Cascade Learning. We conduct empirical analysis of our framework on two real world datasets. The results are encouraging and corroborate our proposed framework, in turn proving that it is an improvement over traditional big data analysis methods that use either Spark or Deep learning as individual elements.
翻译：随着《大数据》的普及,最近在这一领域取得了许多进展。过去几十年,阿帕奇哈多普和阿帕奇公园等框架获得了许多牵引力,并变得非常受欢迎,特别是在工业中。人们越来越清楚地看到,有效的大数据分析是解决人工智能问题的关键。因此,在名为 MLlib 的Spark 框架内,建立了一个多层次图书馆。虽然这个图书馆支持多种机器学习算法,但是,仍然有余地高效地利用Spark 设置来进行高时间密集和计算成本昂贵的程序,如深层次学习。在本文件中,我们提出了一个新的框架,将阿帕奇公园的分散计算能力与利用卡斯卡德学习这一流行概念的深层多层透视器(MLP)的先进机器学习结构结合起来。我们对我们的两个真实世界数据集的框架进行了实证分析。结果正在鼓励和证实我们提议的框架,这反过来证明,它改进了使用Spark或深层学习作为个别要素的传统大数据分析方法。