Stream processing is a computing paradigm that has emerged from the necessity of
handling high volumes of data in real time. In contrast to traditional databases, stream …

TheBig Data'of yesterday is thedata'of today. As technology progresses, new challenges
arise and new solutions are developed. Due to the emergence of Internet of Things …

Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …

This volume focuses on the theory and practice of data stream management, and the novel
challenges this emerging domain poses for data-management algorithms, systems, and …

Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …

The current development towards the Internet of Things introduces the need for more
flexibility in stream processing. To counter these challenges, the authors propose elastic …

In current international context boundaries set for applications are being pushed by the
emergence of bursty and time-varying data streams required to be processed in near real …

In our data-centric society, online services, decision making, and other aspects are
increasingly becoming heavily dependent on trends and patterns extracted from data. A …

The immense growth of data demands switching from traditional data processing solutions
to systems, which can process a continuous stream of real time data. Various applications …

Elastic scaling allows data stream processing systems to dynamically scale in and out to
react to workload changes. As a consequence, unexpected load peaks can be handled and …