The traditional databases are not capable of handling unstructured data and high volumes
of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious …

The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

Machine learning (ML) is widely used to derive useful information from large-scale data
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …

Video cameras are pervasively deployed for security and smart city scenarios, with millions
of them in large cities worldwide. Achieving the potential of these cameras requires …

Large volumes of videos are continuously recorded from cameras deployed for traffic control
and surveillance with the goal of answering “after the fact” queries: identify video frames with …

Real-time analytics on video data demands intensive computation resources and high
energy consumption. Traditional cloud-based video analytics relies on large centralized …

The emerging class of wide-area streaming analytics faces the challenge of scarce and
variable WAN bandwidth. Non-adaptive applications built with TCP or UDP suffer from …

Low latency analytics on geographically distributed datasets (across datacenters, edge
clusters) is an upcoming and increasingly important challenge. The dominant approach of …

The concept of event processing is established as a generic computational paradigm in
various application fields. Events report on state changes of a system and its environment …

Global-scale organizations produce large volumes of data across geographically distributed
data centers. Querying and analyzing such data as a whole introduces new research issues …