03109nam 2200229 n 450 001001100000049001300011100004000024200006800064210003700132300006200169328006400231330230800295336002002603689002902623700002302652702002302675801001802698856014202716997000702858FMT000702865FOR000702872TD16025460 aTDMAGDIG a20190501d2015------k--ita-50----ba 1 aDistributed Computing for Large-scale GraphsbTesi di dottorato 1cUniversity of Trentod2015-12-18 aIn relazione con http://eprints-phd.biblio.unitn.it/1613/ 0btesi di dottoratocINF/01 INFORMATICAeUniversity of Trento aThe last decade has seen an increased attention on large-scale data analysis, caused mainly by the availability of new sources of data and the development of programming model that allowed their analysis. Since many of these sources can be modeled as graphs, many large-scale graph processing frameworks have been developed, from vertex-centric models such as pregel to more complex programming models that allow asynchronous computation, can tackle dynamism in the data and permit the usage of different amount of resources. This thesis presents theoretical and practical results in the area of distributed large- scale graph analysis by giving an overview of the entire pipeline. Data must first be pre-processed to obtain a graph, which is then partitioned into subgraphs of similar size. To analyze this graph the user must choose a system and a programming model that matches her available resources, the type of data and the class of algorithm to execute. Aside from an overview of all these different steps, this research presents three novel approaches to those steps. The first main contribution is dfep, a novel distributed partitioning algorithm that divides the edge set into similar sized partition. dfep can obtain partitions with good quality in only a few iterations. The output of dfep can then be used by etsch, a graph processing framework that uses partitions of edges as the focus of its programming model. etschâ€™s programming model is shown to be flexible and can easily reuse sequential classical graph algorithms as part of its workflow. Implementations of etsch in hadoop, spark and akka allow for a comparison of those systems and the discussion of their advantages and disadvantages. The implementation of etsch in akka is by far the fastest and is able to process billion-edges graphs faster that competitors such as gps, blogel and giraph++, while using only a few computing nodes. A final contribution is an application study of graph-centric approaches to word sense induction and disambiguation: from a large set of documents a word graph is constructed and then processed by a graph clustering algorithm, to find documents that refer to the same entities. A novel graph clustering algorithm, named tovel, uses a diffusion-based approach inspired by the cycle of water. aapplication/pdf0 aINF/01bINFORMATICAcTDR 0aGuerrieri, Alessio 0aMontresor, Alberto 3aITbIT-FI00984 uhttp://memoria.depositolegale.it/*/http://eprints-phd.biblio.unitn.it/1613/1/main.pdf2http://eprints-phd.biblio.unitn.it/1613/1/main.pdf aCF aTD aTD