We present the new distributed-memory run-time system (RTS) of the C++-based open-
source structured parallel programming library FastFlow. The new RTS enables the …

Stream processing applications extract value from raw data through Directed Acyclic Graphs
of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale …

In this article, a new version of the Real-time Image Stream Algorithms (RISA) data
processing suite is introduced. It now features online detector data acquisition, high …

This chapter provides students, industry experts, and researchers a high-level and
comprehensive overview of the end-to-end architectures of big data stream processing …

Abstract Data Stream Processing engines have recently emerged as powerful tools for
simplifying the analysis of network telemetry data. Motivated by the ever-growing volume of …

Several real-world parallel applications are becoming more dynamic and long-running,
demanding online (at run-time) adaptations. Stream processing is a representative scenario …

An increasing number of application domains require high-throughput processing to extract
insights from massive data streams. The Data Stream Processing (DSP) paradigm provides …

Many emerging applications analyze data streams by running graphs of communicating
tasks called operators. To develop and deploy such applications, Stream Processing …

Data Stream Processing engines are emerging as a promising solution to efficiently process
a continuous amount of telemetry information. In this poster, we compare four of them: Storm …

Modern multi-core servers are powerful enough to process multi-gigabit live packet streams
on the network data plane. However, in most cases network programmers must build their …