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 …

The big data samples are important source for analytics. However, its relevant/irrelevant
information, unspecified variables/scales, noise/null, and so forth impose huge challenges …

Given a scatterplot with tens of thousands of points or even more, a natural question is which
sampling method should be used to create a small but “good” scatterplot for a better …

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 …

Scatterplots are among the most widely used visualization techniques. Compelling
scatterplot visualizations improve understanding of data by leveraging visual perception to …

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 …

Due to the development of internet technology and computer science, data is exploding at
an exponential rate. Big data brings us new opportunities and challenges. On the one hand …

Clustering a big dataset without knowing the number of clusters presents a big challenge to
many existing clustering algorithms. In this paper, we propose a Random Sample Partition …

We consider the problem of generating SQL notebooks of comparison queries for
Exploratory Data Analysis (EDA). A comparison query allows to find insights in a dataset by …

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 …