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 …

Scheduling is essentially a decision-making process that enables resource sharing among a
number of activities by determining their execution order on the set of available resources …

In this paper, we focus on general-purpose Distributed Stream Data Processing Systems
(DSDPSs), which deal with processing of unbounded streams of continuous data at scale …

In a distributed stream data processing system, an application is usually modeled using a
directed graph, in which each vertex corresponds to a data source or a processing unit, and …

Recent years have witnessed the booming of big data analytical applications (BDAAs). This
trend provides unrivaled opportunities to reveal the latent patterns and correlations …

Task scheduling for microservice-oriented industrial software is a complex process. It is a
real-time process where multiple task attributes should be considered and different tasks …

In a distributed stream data processing system, an application is usually modeled using a
directed graph, in which each vertex corresponds to a data source or a processing unit, and …

Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant
systems that aggregate and analyze real-time data in motion. To continuously perform …

The emergence of edge stream processing has created a new way of processing real-time
data from the Internet of Things (IoT), which comprises a plethora of geographically …

Scheduling of service-oriented industrial businesses to meet the real-time requirements and
improve the computing resources utilization is important. To this end, a Fine-grained Online …