With the development of the Internet of Things technology, the current amount of data
generated by the Internet of Things system is increasing, and these data are continuously …

Data sampling methods have been investigated for decades in the context of machine
learning and statistical algorithms, with significant progress made in the past few years …

Stratified random sampling (SRS) is a widely used sampling technique for approximate
query processing. We consider SRS on continuously arriving data streams and statically …

Big data streams are possibly one of the most essential underlying notions. However, data
streams are often challenging to handle owing to their rapid pace and limited information …

In this work, we present a comprehensive treatment of weighted random sampling (WRS)
over data streams. More precisely, we examine two natural interpretations of the item …

To deal with large datasets, sampling can be used as a preprocessing step for clustering. In
this paper, an hybrid sampling algorithm is proposed. It is density-based while managing …

In current digital era according to (as far) massive progress and development of internet and
online world technologies such as big and powerful data servers we face huge volume of …

As clustering algorithms become more and more sophisticated to cope with current needs,
large data sets of increasing complexity, sampling is likely to provide an interesting …

Stratified random sampling (SRS) is a widely used sampling technique for approximate
query processing. We consider SRS on continuously arriving data streams, and make the …

The paper purposely to confirm the keys entity from the literature reviews targeting expertise
as advisors for the organisation management to establish an existence required for mitigates …