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

Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

The concept of event processing is established as a generic computational paradigm in
various application fields. Events report on state changes of a system and its environment …

Streaming computations are by nature long-running, and their workloads can change in
unpredictable ways. This in turn means that maintaining performance may require …

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 …

Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …

Many Big Data applications in science and industry have arisen, that require large amounts
of streamed or event data to be analyzed with low latency. This paper presents a reactive …

This paper addresses the problem of designing scaling strategies for elastic data stream
processing. Elasticity allows applications to rapidly change their configuration on-the-fly (eg …

This open access book explores the dataspace paradigm as a best-effort approach to data
management within data ecosystems. It establishes the theoretical foundations and …

Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …