Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

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 …

In the metaverse the physical space and the virtual space co-exist, and interact
simultaneously. While the physical space is virtually enhanced with information, the virtual …

We introduce ChronoStream, a distributed system specifically designed for elastic stateful
stream computation in the cloud. ChronoStream treats internal state as a first-class citizen …

Large quantities of data are generated continuously over time and from disparate sources
such as users, devices, and sensors located around the globe. This results in the need for …

Many applications must ingest rapid data streams and produce analytics results in near-real-
time. It is increasingly common for inputs to such applications to originate from …

A recent trend in stream processing is offloading the computation of decomposable
aggregation functions (DAF) from cloud nodes to geo-distributed fog/edge devices to …

The reactive programming paradigm aims to simplify the development of reactive systems. It
provides abstractions to define time-changing values that are automatically updated by the …

Stream processing has emerged as a useful technology for applications which require
continuous and low latency computation on infinite streaming data. Since stream processing …

Reliability is of critical importance to many applications involving distributed event
processing systems. Especially the use of stateful operators makes it challenging to provide …