A survey on platforms for big data analytics.
A survey on platforms for big data analytics.
复制标题
DOI：
10.1186/s40537-014-0008-6
复制
发表时间：
2015
影响因子：
8.1
通讯作者：
Reddy CK
中科院分区：
文献类型：
--
作者：
Singh D;Reddy CK
本文的主要目的是对可用于执行大数据分析的不同平台进行深入分析。本文调查了可用于大数据分析的不同硬件平台，并根据可扩展性、数据 I/O 速率、容错性、实时处理、支持的数据大小和迭代任务支持等各种指标评估了每个平台的优缺点。除了硬件之外，还讨论了每个平台中使用的软件框架的详细描述及其优点和缺点。这里描述的一些关键特征可能会帮助读者根据他们的计算需求做出正确选择平台的明智决定。使用星级评级表，还讨论了对大数据分析算法至关重要的六个特征中的每一个特征，对不同平台之间进行严格的定性比较。为了更深入地了解每个平台在大数据分析背景下的有效性，还以伪代码的形式描述了各种平台上广泛使用的 k 均值聚类算法的具体实现级别细节。
The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data analytics. This paper surveys different hardware platforms available for big data analytics and assesses the advantages and drawbacks of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, real-time processing, data size supported and iterative task support. In addition to the hardware, a detailed description of the software frameworks used within each of these platforms is also discussed along with their strengths and drawbacks. Some of the critical characteristics described here can potentially aid the readers in making an informed decision about the right choice of platforms depending on their computational needs. Using a star ratings table, a rigorous qualitative comparison between different platforms is also discussed for each of the six characteristics that are critical for the algorithms of big data analytics. In order to provide more insights into the effectiveness of each of the platform in the context of big data analytics, specific implementation level details of the widely used k-means clustering algorithm on various platforms are also described in the form pseudocode.