Various research communities have independently arrived at stream processing as a
programming model for efficient and parallel computing. These communities include digital …

Fog computing (FC) and Internet of Everything (IoE) are two emerging technological
paradigms that, to date, have been considered standing-alone. However, because of their …

Big data stream mobile computing is proposed as a paradigm that relies on the convergence
of broadband Internet mobile networking and real-time mobile cloud computing. It aims at …

Distributed data stream processing systems (DSPSs) such as Storm, Flink, and Spark
Streaming are now routinely used to process continuous data streams in (near) real-time …

In this paper, we present the first comprehensive survey of window types for stream
processing systems which have been presented in research and commercial systems. We …

Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …

Distributed stream processing frameworks are designed to perform continuous computation
on possibly unbounded data streams whose rates can change over time. Devising solutions …

Supporting real-time, cost-effective execution of Complex Event processing applications in
the cloud has been an important goal for many scientists in recent years. Distributed Stream …

Stream processing is important for analyzing continuous streams of data in real time. Sliding-
window aggregation is both needed for many streaming applications and surprisingly hard …

One of the main characteristics that today makes cloud services so popular is their ability to
be elastic, ie, they can adapt their provisioning to variable workloads, thus increasing …