Real-time data processing is a standard requirement in Fog Computing. Dynamically
adapting data stream processing frameworks is an essential functionality to handle time …

Data stream processing is an attractive paradigm for analyzing IoT data at the edge of the
Internet before transmitting processed results to a cloud. However, the relative scarcity of fog …

Next generation applications such as augmented/vir-tual reality, autonomous driving, and
Industry 4.0, have tight latency constraints and produce large amounts of data. To address …

Internet of Things (loT) is leading to the pervasive availability of streaming data about the
physical world, coupled with edge computing infrastructure deployed as part of smart cities …

Abstract In the Big Data era, Data Stream Processing (DSP) applications should be capable
to seamlessly process huge amount of data. Hence, they need to dynamically scale their …

Stream processing answers the need for quickly developing and deploying applications for
real-time processing of continually created data. While its ability to handle a high volume of …

While todays' stream processing applications are typically deployed in the cloud, newly
arising use cases in the context of Internet of Things (IoT) often require low-latency analytics …

Elastic data stream processing enables applications to query and analyze streams of real
time data. This is commonly facilitated by processing the flow of the data streams using a …

An ever increasing number of services requires real-time analysis of collected data streams.
Emerging Fog/Edge computing platforms are appealing for such latency-sensitive …

The data generated by Internet of Things (IoT) devices is constantly increasing. The use of
cloud computing to process this data is associated with network congestion, has high …