Computer clusters with the shared-nothing architecture are the major computing platforms
for big data processing and analysis. In cluster computing, data partitioning and sampling …

Distributed computing frameworks are the fundamental component of distributed computing
systems. They provide an essential way to support the efficient processing of big data on …

With the ever-increasing volume of data, alternative strategies are required to divide big data
into statistically consistent data blocks that can be used directly as representative samples of …

Basket analysis is a prevailing technique to help retailers uncover patterns and associations
of sold products in customer shopping transactions. However, as the size of transaction …

Clustering a big distributed dataset of hundred gigabytes or more is a challenging task in
distributed computing. A popular method to tackle this problem is to use a random sample of …

As the volume of data grows rapidly, storing big data in a single data center is no longer
feasible. Hence, companies have developed two scenarios to store their big data in multiple …

In big data clustering exploration, the situation is paradoxical because there is no prior or
insufficient domain knowledge. Moreover, clustering a big dataset is a challenging task in …

MapReduce is a popular paradigm in distributed computing, but it is not efficient when
executing iterative algorithms over a distributed big dataset due to its heavy data …

Data scientists need scalable methods to explore and clean big data before applying
advanced data analysis and mining algorithms. In this paper, we propose the RSP-Explore …

Distributed data management is a key technology to enable efficient massive data
processing and analysis in cluster-computing environments. Specifically, in environments …