Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies.In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them.To accomplish this task, we adopt a bottom-up network construction approach that combines three years' worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports.Through this representation, we utilize machine learning to assess the relative significance of various port features.Our model examined such features and revealed that geographical characteristics and the port's depth are indicators of a port's importance to the Ports Network.Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to the extent of ports.Our work aims to inform decision-making processes related to port development, resource allocation, and infrastructure planning within the industry.

To assure a timely fruition of media and interactive applications to end users is a complex challenge, especially when potentially spread worldwide, at home or in mobility. It in fact requires a careful placement of the software services on the right computational resources, such that those services are placed as close as possible to end users to mitigate the effect of network on the user experience. In this demo paper, we present a tool that aims to facilitate the placement of latency sensitive applications on computational resources, by considering the geographical positioning of the user demand, the user experience, and the budget limitation of application owners.