A significant number of emerging on-line data analysis applications require the processing of data streams, large amounts of data that get updated continuously, to generate output...
This paper describes Mortar, a distributed stream processing platform for building very large queries across federated systems (enterprises, grids, datacenters, testbeds). Nodes i...
MapReduce and stream processing are two emerging, but different, paradigms for analyzing, processing and making sense of large volumes of modern day data. While MapReduce offers t...
Existing distributed publish/subscribe systems (DPSS) offer loosely coupled and easy to deploy content-based stream delivery services to a large number of users. However, the lack ...
While mapping a streaming (such as multimedia or network packet processing) application onto a specified architecture, an important issue is to determine the input stream rates tha...
Graphics Processing Units (GPUs) present large potential performance gains within stream processing applications over the standard CPU. These performance gains are best realised wh...
Stream processing represents an important class of applications that spans telecommunications, multimedia and the Internet. The implementation of streaming programs in FPGAs has a...
Andrei Hagiescu, Weng-Fai Wong, David F. Bacon, Ro...
Batched stream processing is a new distributed data processing paradigm that models recurring batch computations on incrementally bulk-appended data streams. The model is inspired...
Bingsheng He, Mao Yang, Zhenyu Guo, Rishan Chen, B...
- Due to the runtime flexibility offered by field programmable gate arrays (FPGAs), FPGAs are popular devices for stream processing systems, since many stream processing applicatio...
Currently, stream data processing is an active area of research, which includes everything from algorithms and architectures for stream processing to modelling and analysis of var...