Modern applications for distributed publish/subscribe systems often require stream aggregation capabilities along with rich data filtering. When compared to other distributed systems, aggregation in pub/sub differentiates itself as a complex problem which involves dynamic dissemination paths that are difficult to predict and optimize for a priori, temporal fluctuations in publication rates, and the mixed presence of aggregated and non-aggregated workloads. In this paper, we propose a formalization for the problem of minimizing communication traffic in the context of aggregation in pub/sub. We present a solution to this minimization problem by using a reduction to the well-known problem of minimum vertex cover in a bipartite graph. This solution is optimal under the strong assumption of complete knowledge of future publications. We call the resulting algorithm "Aggregation Decision, Optimal with Complete Knowledge" (ADOCK). We also show that under a dynamic setting without full knowledge, ADOCK can still be applied to produce a low, yet not necessarily optimal, communication cost. We also devise a computationally cheaper dynamic approach called "Aggregation Decision with Weighted Publication" (WAD). We compare our solutions experimentally using two real datasets and explore the trade-offs with respect to communication and computation costs.

Peer-to-peer systems provide scalable content distribution for cheap and resist to censorship attempts. However, P2P networks mainly distribute immutable content and provide poor support for highly dynamic content such as produced by collaborative systems. A new class of algorithms called CRDT (Commutative Replicated Data Type), which ensures consistency of highly dynamic content on P2P networks, is emerging. However, if existing CRDT algorithms support the "edit anywhere, anytime” feature, they do not support the "undo anywhere, anytime” feature. In this paper, we present the Logoot-Undo CRDT algorithm, which integrates the "undo anywhere, anytime” feature. We compare the performance of the proposed algorithm with related algorithms and measure the impact of the undo feature on the global performance of the algorithm. We prove that the cost of the undo feature remains low on a corpus of data extracted from Wikipedia.

Massive collaborative editing becomes a reality through leading projects such as Wikipedia. This massive collaboration is currently supported with a costly central service. In order to avoid such costs, we aim to provide a peer-to-peer collaborative editing system. Existing approaches to build distributed collaborative editing systems either do not scale in terms of number of users or in terms of number of edits. We present the Logoot approach that scales in these both dimensions while ensuring causality, consistency and intention preservation criteria. We evaluate the Logoot approach and compare it to others using a corpus of all the edits applied on a set of the most edited and the biggest pages of Wikipedia.