A survey on platforms for big data analytics

By Dilpreet Singh, Chandan K. Reddy. Journal of Big Data, 2014

447 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/a1e2138595f36a43f9b4caccecfb3ded9ec5748f

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Citations in the corpus

Analysing Transportation Data with Open Source Big Data Analytic Tools (2017, methodology): “Some of the open-source Big Data Analytics Tools are: Mahout [11, 12], MLlib [13, 14], H2O [15], SAMOA [16], and SparkR [17].”
Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, background): “Big data analytics processes consume considerable time to provide feedback and guidelines to users, whereas only a few tools [29] can process huge data sets within reasonable amount of processing time.”
Big Data in the construction industry: A review of present status, opportunities, and future trends (2016, background): “[11] for a detailed explanation on their comparison and selection criterion.”
Fog Computing for Sustainable Smart Cities (2017, methodology): “Data stream analytics are discussed in detail in [Liu et al. 2014; Singh et al. 2016; Singh and Reddy 2015].”
A novel big data analytics framework for smart cities (2019, background): “Authors in [23] specified six characteristics a big data analytics platform should maintain to accommodate with the V challenges of big data, specifically: scalability, I/O performance, fault tolerance, real-time processing, data size and support for iterative tasks.”
Machine Learning With Big Data: Challenges and Approaches (2017, background): “Singh and Reddy [20] considered vertical and horizontal scaling platforms.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, unknown): “[398] D.”
A Survey on Distributed Machine Learning (2019, unknown): “In contrast to prior surveys on distributed machine learning ([120][124]) or related � elds ([153][87][123][122][171][144]) we apply a wholistic view to the problem and discuss the practical aspects of state-of-the-art machine learning from a distributed systems angle.”