The distribution of dynamic information from many sources to many destinations is a key challenge for VANET applications such as cooperative traffic information management or decentralized parking guidance systems. In order for these systems to remain scalable it has been proposed to aggregate the information within the network as it travels from the sources to the destinations. However, so far it has remained unclear by what amount the aggregation scheme needs to reduce the original data in order to be considered scalable. In this paper we prove formally that any suitable aggregation scheme must reduce the bandwidth at which information about an area at distance d is provided to the cars asymptotically faster than 1/d 2 . Furthermore, we constructively show that this bound is tight: for any arbitrary > 0, there exists a scalable aggregation scheme that reduces information asymptotically like 1/d 2+ .

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The wide variety of scientific user communities work with data since many years and thus have already a wide variety of data infrastructures in production today. The aim of this paper is thus not to create one new general data architecture that would fail to be adopted by each and any individual user community. Instead this contribution aims to design a reference model with abstract entities that is able to federate existing concrete infrastructures under one umbrella. A reference model is an abstract framework for understanding significant entities and relationships between them and thus helps to understand existing data infrastructures when comparing them in terms of functionality, services, and boundary conditions. A derived architecture from such a reference model then can be used to create a federated architecture that builds on the existing infrastructures that could align to a major common vision. This common vision is named as 'ScienceTube' as part of this contribution that determines the high-level goal that the reference model aims to support. This paper will describe how a well-focused use case around data replication and its related activities in the EUDAT project aim to provide a first step towards this vision. Concrete stakeholder requirements arising from scientific end users such as those of the European Strategy Forum on Research Infrastructure (ESFRI) projects underpin this contribution with clear evidence that the EUDAT activities are bottom-up thus providing real solutions towards the so often only described 'high-level big data challenges'. The followed federated approach taking advantage of community and data centers (with large computational resources) further describes how data replication services enable data-intensive computing of terabytes or even petabytes of data emerging from ESFRI projects.

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