A large number of distributed applications requires continuous and timely processing of
information as it flows from the periphery to the center of the system. Examples include …

Digital monitoring of physiological signals can allow computer systems to adapt
unobtrusively to users, so as to enhance personalised 'smart'interactions. In recent years …

As users of" big data" applications expect fresh results, we witness a new breed of stream
processing systems (SPS) that are designed to scale to large numbers of cloud-hosted …

This article addresses the profitability problem associated with auto-parallelization of
general-purpose distributed data stream processing applications. Auto-parallelization …

In this paper, we present Spade-the System S declarative stream processing engine. System
S is a large-scale, distributed data stream processing middleware under development at IBM …

Modern Stream Processing Engines (SPEs) process large data volumes under tight latency
constraints. Many SPEs execute processing pipelines using message passing on shared …

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 …

We describe an approach to elastically scale the performance of a data analytics operator
that is part of a streaming application. Our techniques focus on dynamically adjusting the …

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 …

In this paper, we study partitioning functions for stream processing systems that employ
stateful data parallelism to improve application throughput. In particular, we develop …