The Grid is a rapidly developing computing structure that allows components of our information technology infrastructure, computational capabilities, databases, sensors, and people to be shared flexibly as true collaborative tools. Over the last 3 years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed computing systems. After the advances made in distributed system design, collaborative environments, high performance computing and high throughput computing, the Grid is the logical next step.

The new Aims and Scope of FGCS will cover new developments in:

[1] Grid Applications and application support:

    Novel applications
    eScience and eBusiness applications
    Problem solving environments and virtual laboratories
    Grid economy
    Semantic and knowledge based grids
    Collaborative Grids and virtual organizations
    High Performance and high throughput computing on grids
    Complex application workflows
    Scientific, industrial and social implications
    Grids in education

[2] Grid methods and middleware:

    Tools for grid development: monitoring and scheduling
    Distributed dynamic resource management
    Grid- and web-services
    Information management
    Protocols and emerging standards
    Peer to peer and internet computing
    Pervasive computing
    Grid Security

[3] Grid Theory:

    Process specification; program and algorithm design
    Theoretical aspects of wide area communication and computation
    Scaling and performance theory
    Protocol verification

Special Issue on Data Science for Smart Healthcare: Methodologies and Applications
Submission Date: 2020-01-30

Healthcare applications around the world are facing new challenges in responding to trends of aging population, the rise of chronic diseases, resources constraints, and the growing focus of citizens on healthy living and prevention.

Therefore, there is an increasing focus on how to improve the rate of fast and accurate diagnoses, how to reduce costs and outcomes in health systems and how to get people to take more accountability for their own health.

Novel ICT technology are enabling the collection of more data than ever before, including medical health records, people’s vital signs and their lifestyle and data about health systems.

This great amount of data does not immediately result in better healthcare insights, but, on the contrary, if not used properly, it can be a burden to people and result in clinicians spending more time with computers than face to face with patients, or citizens being lost in data they are getting from health trackers and many different sensors, or, again, patients reluctant to accept assistive technologies.

In this perspective Data Science and Machine Learning can provide benefits to both patient and medical professionals, also helping in answering the abovementioned questions related to the large amount of available data.

The objective of this special issue is to attract high-quality research and survey articles that promote research and reflect the most recent advances in addressing Data Science methodologies and applications for Healthcare.

We welcome researchers from both academia and industry to provide their state-of-the-art technologies and ideas covering all aspects of Data Science methodologies and applications for Healthcare.

Topics:

Potential topics include but are not limited to following:

• Artificial Intelligence models for Healthcare.
• Machine Learning models for Healthcare.
• Clustering and classification algorithms for Healthcare.
• Deep and reinforcement learning for Healthcare.
• Big Data analytics for data processing from Healthcare.
• Fuzzy Systems proposals for Healthcare.
• Expert/hybrid Systems for Healthcare
• AI/ML for IoT, Industry 4.0 for Healthcare
• Intelligent security proposals for Healthcare.
• Control systems developments for Healthcare
• Organization Based Multiagent Systems for Healthcare

Special Issue on Co-design of Data and Computation Management in Fog Computing
Submission Date: 2020-01-31

The term Fog Computing has been introduced to identify a paradigm for designing applications able to exploit both the (virtually) infinite resources on the cloud and the limited edge computation power by operating also on the devices living in between these two sides. More specifically, the aim is to exploit a heterogeneous and distributed computational and storage environment to optimize the execution of modern applications requiring high computational resources while reducing the delay and defining constraints on where and how data can be moved and stored. In fact, big volumes of data are produced everyday at the edge of the network and their analysis requires to either move the computation to the data or to move the data to the computation. For this reason, a co-design between data and computation management is required. An example could be to define the amount of data to be moved with respect to the complexity of required data analysis. In some cases, this data movement is not possible or limited due to privacy restrictions, which do not allow data collected at the edge to be stored on cloud facilities maintained by third-party actors. In other cases, the volume of data moved through the network introduces severe delays in the processing and techniques to reduce the volume by pre-processing and filtering them directly where the data are generated should be enacted to reduce this delay. At the same time, any transformation of the data might affect their utility for the final customer, thus, also the quality of the data should be taken into account. Moreover, the data collected by IoT and sensors at the edge is often subject to quality issues that might be detected and solved before using them for analytics and decision making.

This special issue aims to gather recent work on the topic of data and computation management in fog computing. Topics of particular interest include, but are not limited to:

● Management and control of Fog Computing resources and applications
● Data and computation management architectures in Fog Computing
● Data-driven offloading computation
● Security and privacy issues in data and computation management in Fog Computing
● Data movement, data duplication, and data distribution
● Energy efficiency in Fog Computing through data and computation movement
● Data quality assessment and improvement in Fog Computing
● Data processing in heterogeneous and/or distributed environments
● Internet of Things and Fog Computing
● Smart monitoring and anomaly detection for Fog resources and applications
● Simulation tools for Fog Computing
● Data processing platforms for Fog Computing
● Experiment-driven research on data and processing placement