Abstract The Internet of Things (IoT) is a paradigm based on the Internet that comprises
many interconnected technologies like RFID (Radio Frequency IDentification) and WSAN …

The traditional databases are not capable of handling unstructured data and high volumes
of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious …

Serverless computing is poised to fulfill the long-held promise of transparent elasticity and
millisecond-level pricing. To achieve this goal, service providers impose a finegrained …

Adaptive machine learning models are revolutionizing real-time financial fraud prevention in
dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud …

The need for scalable and efficient stream analysis has led to the development of many
open-source streaming data processing systems (SDPSs) with highly diverging capabilities …

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 …

Distributed stream processing systems need to support stateful processing, recover quickly
from failures to resume such processing, and reprocess an entire data stream quickly. We …

Stream processors are emerging in industry as an apparatus that drives analytical but also
mission critical services handling the core of persistent application logic. Thus, apart from …

Evidence suggests that the proportion of the human population living in cities will continue to
grow, to the point where over 90% of the worldLs population will be living in one form of city …

MillWheel is a framework for building low-latency data-processing applications that is widely
used at Google. Users specify a directed computation graph and application code for …