دانلود رایگان مقاله انگلیسی یک بررسی تجربی در چارچوب داده های بزرگ - الزویر 2018

abstract

Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.

6. Conclusions

In this work, we surveyed popular frameworks for large-scale data processing. After a brief description of the main paradigms related to Big Data problems, we presented an overview of the Big Data frameworks Hadoop, Spark, Storm and Flink. We presented a categorization of these frameworks according to some main features such as the used programming model, the type of data sources, the supported programming languages and whether the framework allows iterative processing or not. We also conducted an extensive comparative study of the above presented frameworks on a cluster of machines and we highlighted best practices while using the studied Big Data frameworks.