Our main goal is to create a visual analysis system for the exploration of dynamic or time-dependent networks (from small to large scale). Our contributions will be in three principle areas: (A) novel algorithms for network clustering that are based on graph harmonic analysis and level-of-detail methods; (B) the development of novel similarity measures for networks and network clusters for the purpose of comparing multiple network clusterings and the grouping (clustering) of different network clusterings; and (C) a system for user-driven analysis of network clusterings supported by novel visual encodings and interaction techniques suitable for exploring dynamic networks and their clusterings in the presence of uncertainties due to noise and uncontrolled variations of network properties. Our aim is to make these novel algorithms accessible to a broad range of users and researchers to enable reliable and informed decisions based on the network analysis. A focus in all three areas will be on the incorporation of uncertainty into the analysis and visual encoding to enhance the trust in the decision making. While we are aiming to create tools for a variety of use cases, we specifically focus on two application areas -- social networks such as Twitter as well as brain functional networks. These are two applications where the consortium has a lot of expertise, yet which are very different in terms of users and tasks. Hence, we hope to be able to generalize from these two specific applications.Our team consists of three distinct research labs with expertise in harmonic analysis (Dr. Van De Ville, EPFL, Switzerland), expertise in network visualization (Dr. Fekete, Inria France) and expertise in supporting of visual model building and analysis under uncertainty (Dr. Möller, Uni Wien, Austria). This is a unique combination of skills that is indispensable to successfully tackle the challenges of this endeavour made possible only under the unique requirements of this funding call.

Finding new ways to manage the increased data usage and to improve the level of service required by the new wave of smartphones applications is an essential issue. MACACO project proposes an innovative solution to this problem by focusing on data offloading mechanisms that take advantage of context and content information. Our intuition is that if it is possible to extract and forecast the behaviour of mobile network users in the three-dimensional space of time, location and interest (i.e. ‘what data’, ‘when’ and ‘where’ users are pulling data from the network), it is possible to derive efficient data offloading protocols. Such protocols would pre-fetch the identified data and cache it at the network edge at an earlier time, preferably when the mobile network is less charged, or offers better quality of service. Caching can be done directly at the mobile terminals, but as well at the edge nodes of the network (e.g., femtocells or wireless access points). Building on previous research efforts in the fields of social wireless networking, opportunistic communications and content networking, MACACO will address several issues. The first one is to derive appropriate models for the correlation between user interests and their mobility. Lots of studies have characterized mobile nodes mobility based on real world data traces, but knowledge about the interactions with user interests in this context is still missing. To fill this gap, MACACO proposes to acquire real world data sets to model mobile node behaviour in the aforementioned three-dimensional space. The second issue addressed is the derivation of efficient data-offloading algorithms leveraging the large-scale data traces and corresponding models. Firstly, simple and efficient prediction algorithms will be derived to forecast the node’s mobility and interests. Then, MACACO has to output data pre-fetching mechanisms that both improves the perceived quality of service of the mobile user and noticeably offloads pick bandwidth demands at the cellular network. A proof of concept will be exhibited though a federated testbed located in France, Switzerland and in the UK. The consortium was carefully constituted to gather partners that are pretty complementary and qualified to address the context-content correlation and related data offloading challenge. The partners of MACACO will combine research and experience in a wide set of areas to gain unique competence, which will be brought forward to other European partners through the dissemination and exploitation activities of the consortium.

Finding new ways to manage the increased data usage and to improve the level of service required by the new wave of smartphones applications is an essential issue. MACACO project proposes an innovative solution to this problem by focusing on data offloading mechanisms that take advantage of context and content information. Our intuition is that if it is possible to extract and forecast the behaviour of mobile network users in the three-dimensional space of time, location and interest (i.e. ‘what data’, ‘when’ and ‘where’ users are pulling data from the network), it is possible to derive efficient data offloading protocols. Such protocols would pre-fetch the identified data and cache it at the network edge at an earlier time, preferably when the mobile network is less charged, or offers better quality of service. Caching can be done directly at the mobile terminals, but as well at the edge nodes of the network (e.g., femtocells or wireless access points). Building on previous research efforts in the fields of social wireless networking, opportunistic communications and content networking, MACACO will address several issues. The first one is to derive appropriate models for the correlation between user interests and their mobility. Lots of studies have characterized mobile nodes mobility based on real world data traces, but knowledge about the interactions with user interests in this context is still missing. To fill this gap, MACACO proposes to acquire real world data sets to model mobile node behaviour in the aforementioned three-dimensional space. The second issue addressed is the derivation of efficient data-offloading algorithms leveraging the large-scale data traces and corresponding models. Firstly, simple and efficient prediction algorithms will be derived to forecast the node’s mobility and interests. Then, MACACO has to output data pre-fetching mechanisms that both improves the perceived quality of service of the mobile user and noticeably offloads pick bandwidth demands at the cellular network. A proof of concept will be exhibited though a federated testbed located in France, Switzerland and in the UK. The consortium was carefully constituted to gather partners that are pretty complementary and qualified to address the context-content correlation and related data offloading challenge. The partners of MACACO will combine research and experience in a wide set of areas to gain unique competence, which will be brought forward to other European partners through the dissemination and exploitation activities of the consortium.