A recurrent challenge in real‐world applications is autonomous management of the
executions at run‐time. In this vein, stream processing is a class of applications that compute …

Accelerating SQL queries on stream processing by utilizing heterogeneous coprocessors,
such as GPUs, has shown to be an effective approach. Most works show that heterogeneous …

Exploring the sharing opportunities among multiple stream queries is crucial for high-
performance stream processing. Modern stream processing necessitates accelerating …

Stream processing is a parallel paradigm used in many application domains. With the
advance of graphics processing units (GPUs), their usage in stream processing applications …

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 …

High performance stream aggregation is critical for many emerging applications that analyze
massive volumes of data. Incoming data needs to be stored in a sliding window during …

High throughput stream aggregation is essential for many applications that analyze massive
volumes of data. Incoming data need to be stored in a sliding window before processing, in …

Lossy compression is becoming an indispensable technique for the success of today's
extreme-scale high-performance computing projects that produce vast volumes of data …

While GPUs have been utilized in the analysis stage of big data processing, the demand for
GPU use in the extract-transform-load (ETL) stage has recently been increasing. There have …

High performance stream aggregation is critical for many emerging applications that analyze
massive volumes of data. Incoming data needs to be stored in a sliding-window before …