Event processing (EP) is a data processing technology that conducts online processing of
event information. In this survey, we summarize the latest cutting-edge work done on EP …

Systems enabling the continuous processing of large data streams have recently attracted
the attention of the scientific community and industrial stakeholders. Data Stream Processing …

To achieve high energy efficiency and low response time in big data stream computing
environments, it is required to model an energy-efficient resource scheduling and …

Big data streaming has become an important paradigm for real-time processing of massive
continuous data flows in large scale sensing networks. While dealing with big sensing data …

Applications in risk-critical domains such as emergency management and industrial control
systems need near-real-time stream data processing in large-scale sensing networks. The …

Resource constrained sensing devices are being used widely to build and deploy self-
organizing wireless sensor networks for a variety of critical applications such as smart cities …

The near real-time processing of continuous data flows in large scale sensor networks has
many applications in risk-critical domains ranging from emergency management to industrial …

Online scheduling plays a key role for big data streaming applications in a big data stream
computing environment, as the arrival rate of high-velocity continuous data stream might …

Abstract We present R-Stream ⋅ TF, a polyhedral optimization tool for neural network
computations. R-Stream ⋅ TF transforms computations performed in a neural network graph …

The issue of high system stability is one of the major obstacles for real-time computing over
fluctuating big data streams. A stable scheduling is more important than an efficient …