Andrea Ribichini

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Data stream processing has recently received increasing attention as a computational paradigm for dealing with massive data sets. Surprisingly, no algorithm with both sublinear space and passes is known for natural graph problems in classical read-only streaming. Motivated by technological factors of modern storage systems, some authors have recently(More)
In this paper we show how parallel algorithms can be turned into efficient streaming algorithms for several classical combinatorial problems in the W-Stream model. In this model, at each pass one input stream is read, one output stream is written, and data items have to be processed using limited space; streams are pipelined in such a way that the output(More)
Dataflow languages provide natural support for specifying constraints between objects in dynamic applications, where programs need to react efficiently to changes in their environment. In this article, we show that one-way dataflow constraints, largely explored in the context of interactive applications, can be seamlessly integrated in any imperative(More)
This article reports the results of an extensive experimental analysis of efficient algorithms for computing graph spanners in the data streaming model, where an (α,β)-spanner of a graph G is a subgraph S⊆G such that for each pair of vertices the distance in S is at most α times the distance in G plus β. To the best of our knowledge, this is the first(More)
We introduce and investigate a new notion of resilience in graph spanners. Let $$S$$ S be a spanner of a weighted graph $$G$$ G . Roughly speaking, we say that $$S$$ S is resilient if all its point-to-point distances are resilient to edge failures. Namely, whenever any edge in $$G$$ G fails, then as a consequence of this failure all distances do not degrade(More)
In this report I will focus on the research activity carried on during the first two years of my PhD program, at the University of Rome " La Sapienza " , in the field of massive data sets, with particular emphasis on the streaming computational model: in this model data stored in external memory can be accessed only sequentially, in one or several passes,(More)
In this paper we show how PRAM algorithms can be turned into efficient streaming algorithms for several classical combinatorial problems in the W-Stream model. In this model, at each pass one input stream is read and one output stream is written ; streams are pipelined in such a way that the output stream produced at pass i is given as input stream at pass(More)