Serverless computing (Function-as-a-Service) provides fine-grain resource sharing by
running functions (or Lambdas) in containers. Data-dependent functions are required to be …

Several software systems are built upon stream processing architectures to process large
amounts of data in near real-time. Today's distributed stream processing systems (DSPSs) …

Recently, increasingly large amounts of data are generated from a variety of sources.
Existing data processing technologies are not suitable to cope with the huge amounts of …

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

The amalgamation of artificial intelligence with Internet of Things (AIoT) devices have seen a
rapid surge in growth, largely due to the effective implementation of deep neural network …

Subgraph enumeration is important for many applications such as network motif discovery
and community detection. Recent works utilize graphics processing units (GPUs) to …

Systems enabling the continuous processing of large data streams have recently attracted
the attention of the scientific community and industrial stakeholders. Data Stream Processing …

Context: The combination of distributed stream processing with microservice architectures is
an emerging pattern for building data-intensive software systems. In such systems, stream …

Stream Processing Engines (SPEs) execute long-running queries on unbounded data
streams. They follow an interpretation-based processing model and do not perform runtime …

We introduce BriskStream, an in-memory data stream processing system (DSPSs)
specifically designed for modern shared-memory multicore architectures. BriskStream's key …