Organizations deploy a hierarchy of clusters-cameras, private clusters, public clouds-for
analyzing live video feeds from their cameras. Video analytics queries have many …

Pattern queries are widely used in complex event processing (CEP) systems. Existing
pattern matching techniques, however, can provide only limited performance for expensive …

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

Stream processing is gaining importance as more data becomes available in the form of
continuous streams and companies compete to promptly extract insights from them. In such …

Many stream-processing systems enforce an order on data streams during query evaluation
to help unblock blocking operators and purge state from stateful operators. Such in-order …

Large-scale data analysis has become increasingly important for many enterprises.
Recently, a new distributed computing paradigm, called MapReduce, and its open source …

Applications ranging from algorithmic trading to scientific data analysis require realtime
analytics based on views over databases that change at very high rates. Such views have to …

Stream processing is important for analyzing continuous streams of data in real time. Sliding-
window aggregation is both needed for many streaming applications and surprisingly hard …

Applications ranging from algorithmic trading to scientific data analysis require real-time
analytics based on views over databases receiving thousands of updates each second …

In this paper, we present the first comprehensive survey of window types for stream
processing systems which have been presented in research and commercial systems. We …