Public edge platforms have drawn increasing attention from both academia and industry. In
this study, we perform a first-of-its-kind measurement study on a leading public edge …

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 …

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

In multi-tenant systems, the CPU overhead of distributed file systems (DFSes) is increasingly
a burden to application performance. CPU and memory interference cause degraded and …

Fine-grained traffic flow records enable many powerful applications, especially in
combination with telemetry systems that supports high coverage, ie, of every link and at all …

Many Internet of Things (IoT) applications would benefit if streams of data could be analyzed
rapidly at the Edge, near the data source. However, existing Stream Processing Engines …

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

The arising Internet of Things (IoT) will require significant changes to current stream
processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present …

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

Window aggregation queries are a core part of streaming applications. To support window
aggregation efficiently, stream processing engines face a trade-off between exploiting …