We present a system for allocating resources in shared data and compute clusters that
improves MapReduce job scheduling in three ways. First, the system uses regulated and …

Recently MapReduce programming model is becoming popular for large scale data
intensive distributed applications due to its efficiency, simplicity and ease of use. The …

A typical MapReduce cluster is shared among different users and multiple applications. A
challenging problem in such shared environments is the ability to efficiently control resource …

FPGAs (Field Programmable Gate Arrays) are making their way into data centers (DC). They
are used as accelerators to boost the compute power of individual server nodes and to …

Classification is a significant technique in data mining research and applications. C4. 5 is a
widely used classification method, and ensemble learning adopts a parallel and distributed …

MapReduce is a parallel programming model to process large datasets, and it was inspired
by the Map and Reduce primitives from functional languages. Its first implementation was …

Big Data applications are characterized by a non‐negligible number of complex parallel
transactions on a huge amount of data that continuously varies, generally increasing over …

Scale of data generated and processed is exploding in the Big Data era. The MapReduce
system popularized by open-source Hadoop is a powerful tool for the exploding data …

Mining the popular SMS messages in a short period of time is very valuable. However,
traditional OLAP-based mining method is not suitable for this very large scale dataset. In this …

In the field of data mining, clustering is one of the important methods. K-Means is a typical
distance-based clustering algorithm; 2-tier clustering should implement scalable clustering …