Baran

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.