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 …

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 …

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

Fuzzy complex event processing-based decision-making systems have received
considerable research interests recently. In particular, a well-managed operator distribution …

Today we are witnessing a dramatic shift toward a data-driven economy, where the ability to
efficiently and timely analyze huge amounts of data marks the difference between industrial …

Effective overload control for large-scale online service system is crucial for protecting the
system backend from overload. Conventionally the design of overload control is ad-hoc for …

In recent years, a new class of applications has come to the forefront {p-marily due to the
advancement in our ability to collect data from multitudes of devices, and process them e …

This paper describes the SODA scheduler for System S, a highly scalable distributed stream
processing system. Unlike traditional batch applications, streaming applications are open …

Optimally assigning streaming tasks to network machines is a key factor that influences a
large data-stream-processing system's performance. Although researchers have prototyped …

We present a collaborative, self-configuring high availability (HA) approach for stream
processing that enables low-latency failure recovery while incurring small run-time …