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Baran: An Effective MapReduce-Based Solution to Solve Big Data Problems

Baran: An Effective MapReduce-Based Solution to Solve Big Data Problems
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Author(s): Mohammadhossein Barkhordari (Information and Communication Technology Research Center, Iran), Mahdi Niamanesh (Information and Communication Technology Research Center, Iran) and Parastoo Bakhshmandi (Information and Communication Technology Research Center, Iran)
Copyright: 2019
Pages: 38
Source title: Handbook of Research on the Evolution of IT and the Rise of E-Society
Source Author(s)/Editor(s): Maki Habib (The American University in Cairo, Egypt)
DOI: 10.4018/978-1-5225-7214-5.ch007

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Abstract

The MapReduce method is widely used for big data solutions. This method solves big data problems on distributed hardware platforms. However, MapReduce architectures are inefficient. Data locality, network congestion, and low hardware performance are the main issues. In this chapter, the authors introduce a method that solves these problems. Baran is a method that, if an algorithm can satisfy its conditions, can dramatically improve performance and solve the data locality problem and consequences such as network congestion and low hardware performance. The authors apply this method to previous works on data warehouse, graph, and data mining problems. The results show that applying Baran to an algorithm can solve it on the MapReduce architecture properly.

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