Matteo Ceccarello

Automated generation of model classes for Java PathFinder

Model checkers like Java PathFinder (JPF) often have to combat the state space explosion problem. One solution adopted to tackle this problem is to abstract away parts of the system, e. g., to model complex library classes at a higher level of abstraction. The model classes have the same interface as the actual library classes but exhibit reduced be- haviour and state. Writing such model classes is both error prone and time consuming. In this paper we propose a tool that can automatically derive a model class from the original class. To achieve this goal, the tool uses different algorithms, including slicing and value generation, each yielding a model class with different behaviour and state.

Space and Time Efficient Parallel Graph Decomposition, Clustering, and Diameter Approximation

We develop a novel parallel decomposition strategy for unweighted, undirected graphs, based on growing disjoint connected clusters from batches of centers progressively selected from yet uncovered nodes. With respect to similar previous decompositions, our strategy exercises a tighter control on both the number of clusters and their maximum radius. We present two important applications of our parallel graph decomposition: (1)

MapReduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension

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A Practical Parallel Algorithm for Diameter Approximation of Massive Weighted Graphs

-center clustering approximation; and (2) diameter approximation. In both cases, we obtain algorithms which feature a polylogarithmic approximation factor and are amenable to a distributed implementation that is geared for massive (long-diameter) graphs. The total space needed for the computation is linear in the problem size, and the parallel depth is substantially sublinear in the diameter for graphs with low doubling dimension. To the best of our knowledge, ours are the first parallel approximations for these problems which achieve sub-diameter parallel time, for a relevant class of graphs, using only linear space. Besides the theoretical guarantees, our algorithms allow for a very simple implementation on clustered architectures: we report on extensive experiments which demonstrate their effectiveness and efficiency on large graphs as compared to alternative known approaches.

Experimental evaluation of multi-round matrix multiplication on MapReduce

Given a dataset of points in a metric space and an integer

Tools to generate and check consistency of model classes for Java PathFinder

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Fast Coreset-based Diversity Maximization under Matroid Constraints

, a diversity maximization problem requires determining a subset of

Fast Coreset-Based Max-Sum Diversity under Matroid Constraints

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Solving

points maximizing some diversity objective measure, e.g., the minimum or the average distance between two points in the subset. Diversity maximization is computationally hard, hence only approximate solutions can be hoped for. Although its applications are mainly in massive data analysis, most of the past research on diversity maximization focused on the sequential setting. In this work we present space and pass/round-efficient diversity maximization algorithms for the Streaming and MapReduce models and analyze their approximation guarantees for the relevant class of metric spaces of bounded doubling dimension. Like other approaches in the literature, our algorithms rely on the determination of high-quality core-sets, i.e., (much) smaller subsets of the input which contain good approximations to the optimal solution for the whole input. For a variety of diversity objective functions, our algorithms attain an

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(\alpha+\epsilon)