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 …

The inherently large and varying volumes of information generated in large scale systems
demand near real-time processing of data streams. In this context, data streaming is …

In distributed digitalized environments in the context of the Internet of Things, we often need
to do an analysis of big data originating at high rate-sensors at the edge of the infrastructure …

The data streaming paradigm was introduced around the year 2000 to overcome the
limitations of traditional store-then-process paradigms found in relational databases (DBs) …

Applications for adaptive (sometimes also called smart) Cyber-Physical Systems are
blossoming thanks to the large volumes of data, sensed in a continuous fashion, in large …

Stream processing applications extract value from raw data through Directed Acyclic Graphs
of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale …

Streaming applications continuously process data to deliver streams of up-to-date results.
Their growing adoption for data analysis in many distributed systems is motivated by their …

Fine-grained data provenance in data streaming allows linking each result tuple back to the
source data that contributed to it, something beneficial for many applications (eg, to find the …

With the development of communication technology and artificial intelligence, the
application of IoT (Internet of Things) has driven the development of smart cities. Real-time …

The growing interest in data analysis applications for Cyber–Physical Systems stems from
the large amounts of data such large distributed systems sense in a continuous fashion. A …