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

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

Single-node multi-core stream processing engines (SPEs) can process hundreds of millions
of tuples per second. Yet making them fault-tolerant with exactly-once semantics while …

Over the last decades, the data processing environment significantly changed. Nowadays,
data-centric applications process ever-growing volumes of data with increasing velocity. At …

Over the past two decades, distributed stream processing engines (SPEs) have become a
prominent component in the big data management tool-chain to support real-time, stateful …

With increasing data volumes and velocity, many applications are shifting from the classical
“process-after-store” paradigm to a stream processing model: data is produced and …

Clusters that handle data-intensive workloads at a data-centre scale have become
commonplace. In this setting, clusters are typically shared across several users and …

Over the last decades, the data processing environment significantly changed. Nowadays,
data-centric applications process ever-growing volumes of data with increasing velocity. At …