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Andrea Pietracaprina
Researcher at University of Padua
Publications - 107
Citations - 1405
Andrea Pietracaprina is an academic researcher from University of Padua. The author has contributed to research in topics: Parallel algorithm & Approximation algorithm. The author has an hindex of 19, co-authored 102 publications receiving 1281 citations. Previous affiliations of Andrea Pietracaprina include University of Illinois at Urbana–Champaign & Texas A&M University.
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Mining Frequent Itemsets using Patricia Tries.
TL;DR: A depth-first algorithm that discovers all frequent itemsets in a dataset, for a given support threshold, is presented and several experimental results are reported, which assess the effectiveness of the implementation and show the better performance attained by PatriciaMine with respect to other prominent algorithms.
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Space-round tradeoffs for MapReduce computations
TL;DR: A computational model for MapReduce is formally specified which captures the functional flavor of the paradigm by allowing for a flexible use of parallelism and diverges from a traditional processor-centric view by featuring parameters which embody only global and local memory constraints.
Proceedings ArticleDOI
BSP vs LogP
TL;DR: Within the limits of the analysis that is mainly of asymptotic nature, BSP and LogP can be viewed as closely related variants within the bandwidth-latency framework for modeling parallel computation.
BookDOI
Algorit[h]ms - ESA '98 : 6th Annual European Symposium, Venice, Italy, August 24-26, 1998 : proceedings
TL;DR: Invited Lectures External Memory Algorithms Jeffrey S. Vitter Design and Analysis of Dynamic Processes: A Stochastic Approach Car-Pooling as a Data Structuring Device: The Soft Heap Bernard Chazelle Optimal Prefix-Free Codes for Unequal Letter Costs: Dynamic Programming with the Monge Property Wojciech Rytter.
Proceedings ArticleDOI
Tight bounds on information dissemination in sparse mobile networks
TL;DR: Quite surprisingly, it is shown that for a system below the percolation point, the broadcast time does not depend on the transmission radius, and this result complements a recent result of Peres et al. (SODA 2011) who showed that above the perColation point the broadcastTime is polylogarithmic in k.