Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

Automation is the key to enable an efficient, fast, and reliable deployment of applications.
Therefore, several deployment automation technologies emerged in recent years whereby …

The increasing number and diversity of telecommunication services cause a strong need for
automatic configuration management tools for service providers. Combining configuration …

The growing usage of software and hardware in our everyday lives has lead to paradigms
such as Cloud Computing, Edge Computing, and the Internet of Things. The combination of …

In recent years, many different deployment automation technologies have been developed
to automatically deploy cloud applications. Most of these technologies employ declarative …

As the Internet of Things (IoT) evolves rapidly across various industries, the number of IoT
protocols and applications is growing with vast number of heterogeneous components and …

Industrial processes can benefit considerably from utilizing cloud applications that combine
cross-domain knowledge from multiple involved partners. Often, development of such …

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock complete potentials of IoT with intelligence, and IoT applications …

Machine learning (ML) continues to permeate all layers of academia, industry and society.
Despite its successes, mental frameworks to capture and represent machine learning …

Nowadays, Internet of Things (IoT) application development must deal with a variety of
devices, wireless protocols, types of gateways and particularities about each sensor. This …