With the increase in the amount of data captured during the manufacturing process,
monitoring systems are becoming important factors in decision making for management …

Several software systems are built upon stream processing architectures to process large
amounts of data in near real-time. Today's distributed stream processing systems (DSPSs) …

Abstract Context: The Data Stream Processing (DSP) approach focuses on real-time data
processing by applying specific techniques for capturing and processing relevant data for on …

The outbreak of recent pandemic COVID 19 has tempered the world's activities which has
lead to complete collapse of economy at global level. In order to cope with the current …

The importance of Big Data is nowadays established, both in industry and research fields,
especially stream processing for its capability to analyze continuous data streams and …

The volume and type of streaming data is increasing rapidly, thus, real-time processing
scenarios for streaming data have continued to increase. The inherent volatility of streaming …

In this paper, an on-line parallel analytics framework is proposed to process and store in
transit all the data being generated by a Molecular Dynamics (MD) simulation run using …

In recent years, a series of real-life problems are being solved by the leading role of sensors
and the Internet. Smart towns, smart health structures, smart construction, smart landscapes …

Some mission critical systems, eg, fraud detection, require accurate, real-time metrics over
long time sliding windows on applications that demand high throughput and low latencies …

Distributed data processing platforms (eg, Hadoop, Spark, and Flink) are widely used to
distribute the storage and processing of data among computing nodes of a cloud. The …