Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

In present times, data science become popular to support and improve decision-making
process. Due to the accessibility of a wide application perspective of data streaming, class …

Sensors in smart-item environments capture data about product conditions and usage to
support business decisions as well as production automation processes. A challenging …

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

Incremental and approximate computations are increasingly being adopted for data
analytics to achieve low-latency execution and efficient utilization of computing resources …

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 …

With the availability of a broad range of applications for Big Data streaming, both the class
imbalance and concept drift have become crucial learning issues. The concept of drift …

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 the data volume grows, reducing the query execution times remains an elusive goal.
While approximate query processing (AQP) techniques present a principled method to trade …

Reservoir sampling is a well-known technique for random sampling over data streams. In
many streaming applications, however, an input stream may be naturally heterogeneous, ie …