It contains an implementation of the MapReduce model. It divides the task into two functions: Mapper and Reducer, and uses a key-value store for efficient communication between the master and ...

The mapper and reducer functions are implemented in mapreduce/task.h, they are wrapped by the wrapper functions implemented in mapreduce/task_wrapper.h, so that the users can focus only on the ...

MapReduce was invented by Google in 2004, made into the Hadoop open source project by Yahoo! in 2007, and now is being used increasingly as a massively parallel data processing engine for Big Data.

Abstract: MapReduce has been widely used in many data science applications. It has been observed that an excessive data transfer has a negative impact on its performance. To reduce the amount of data ...

MapReduce was introduced by Google to manage large-scale data processing across clusters of servers. The framework processes vast amounts of data in parallel in a reliable and fault-tolerant manner.

Abstract: When there are noises and outliers in the data, the traditional k-medoids algorithm has good robustness, however, that algorithm is only suitable for medium and small data set for its ...

In this paper we study a seismic sensing platform using Shakebox, a low-noise and low-power 24- bit wireless accelerometer sensor. The advances of wireless sensor offer the potential to monitor ...