Window aggregations and windowed joins are central operators of modern real-time
analytic workloads and significantly impact the performance of stream processing systems …

Data stream processing systems (DSPSs) enable users to express and run stream
applications to continuously process data streams. To achieve realtime data analytics …

Sliding-window aggregation is a foundational stream processing primitive that efficiently
summarizes recent data. The state-of-the-art algorithms for sliding-window aggregation are …

Sliding-window aggregation is a foundational stream processing primitive that efficiently
summarizes recent data. The state-of-the-art algorithms for sliding-window aggregation are …

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

An aggregation is a function from a collection of data items to an aggregate value. In
slidingwindow aggregation, the input collection consists of a window over the most recent …

Sliding-window aggregation is a widely-used approach for extracting insights from the most
recent portion of a data stream. While aggregations of interest can usually be expressed as …

The article presents a simple yet optimal approach to compute aggregates of window
queries over data streams. The proposal is built as a fully-fledged pipeline of operators that …

Aggregation queries are a series of computationally-demanding analytics operations on
grouped and/or time series (streaming) data. They include tasks such as summation or …

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 …