Under several emerging application scenarios, such as in smart cities, operational
monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous …

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

Stream Processing (SP) has evolved as the leading paradigm to process and gain value
from the high volume of streaming data produced, eg, in the domain of the Internet of Things …

A recurrent challenge in real‐world applications is autonomous management of the
executions at run‐time. In this vein, stream processing is a class of applications that compute …

The article presents variants for building a cognitive early warning system about a computer
attack on the information resources of the Russian Federation on the basis of Big Data …

Data stream processing systems (DSPSs) enable users to express and run stream
applications to continuously process data streams. To achieve realtime data analytics …

This paper introduces ZeroTune, a novel cost model for parallel and distributed stream
processing that can be used to effectively set initial parallelism degrees of streaming …

Big data is revolutionizing how all sectors of our economy do business, including
telecommunication, transportation, medical, and finance. Big data comes in two flavors: data …

The ability to promptly and efficiently detect arbitrarily complex patterns in massive real-time
data streams is a crucial requirement in many modern applications. The ever-growing scale …

Distributed data stream processing applications are structured as graphs of interconnected
modules able to ingest high-speed data and to transform them in order to generate results of …