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Abstract. Stream processing systems have evolved into established solutions as standalone engines but they still lack flexibility in terms of large-scale deployment, integration, extensibility, and interoperability. In the last years, a substantial ecosystem of new applications has emerged that can potentially benefit from stream processing but introduces different requirements on how stream processing solutions can be integrated, deployed, extended, and federated. To address these needs, we present an exoengine architecture and the associated ExoP platform. Together, they provide the means for encapsulating components of stream processing systems as well as automating the data exchange between components and their distributed deployment. The proposed solution can be used, e.g., to connect heterogeneous streaming engines, replace operators at runtime, and migrate operators across machines with a negligible overhead.

Advances in digital devices, computing, and networking have led to an ever increasing number of personal information being exchanged across the globe. Typically, this is done through centralized, web-based applications like Flickr, YouTube, Twitter, or Facebook. In this paper we propose an alternative architecture for the dissemination of personal information at a global scale. Our solution runs both within a data center as well as on a pool of personal devices such as mobile phones, desktop and laptop computers, or Internet gateways. Our approach leverages idle resources available in millions of devices, allows for much more flexible applications than the predefined services available on the web, and permits users to exchange personal information in a peer-to-peer manner with the possibility but not the requirement to store the personal data in a data center.