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

Big data processing systems are evolving to be more stream oriented where each data
record is processed as it arrives by distributed and low-latency computational frameworks on …

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 …

Many" big data" applications must act on data in real time. Running these applications at
ever-larger scales requires parallel platforms that automatically handle faults and stragglers …

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 …

The first edition of this book appeared in 1991 when the technology was new and there were
not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker …

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 …

Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby
massive amounts of heterogeneous data are gathered from multiple sources, managed …

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

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 …