Human beings keep exploring the physical space using information means. Only recently,
with the rapid development of information technologies and the increasing accumulation of …

In the era of global-scale services, organisations produce huge volumes of data, often
distributed across multiple data centres, separated by vast geographical distances. While …

Parallel computing is an effective method to solve computationally large and data‐intensive
problems. The traditional data mining algorithm cannot mining association rules for large …

Because of the rapid increase of mobile traffic, flexible broadband supportive unmanned
aerial vehicle (UAV)‐based 6G mobile networks using free space optical (FSO) links have …

Geo-distributed data analytics has been proposed to analyze geographically distributed
data. Existing studies have achieved significant reductions in execution time and data …

Distributed streaming applications require the underlying network flows to transmit packets
continuously to keep their output results fresh. These results will become stale if no updates …

Big data analytics platforms have played a critical role in the unprecedented success of data-
driven applications. However, real-time and streaming data applications, and recent …

Nowadays, Spark Streaming, a computing framework based on Spark, is widely used to
process streaming data such as social media data, IoT sensor data or web logs. Due to the …

Abstract Many organizations leverage Distributed Stream processing systems (DPSs) to get
insights from the data generated by different users/devices, eg, the Internet of Things (IoT) …

In the face of rising global demand for video meetings, managing traffic across
geographically distributed (geo-distributed) data centers presents a significant challenge …