A. Pietracaprina

Publications111
h-index18
Citations1,195
Highly Influential Citations76
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Mining Frequent Itemsets using Patricia Tries
TLDR
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.
BSP vs LogP
TLDR
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.
Clustering Uncertain Graphs
TLDR
The NP-hardness of maximizing the minimum connection probability, even in the presence of an oracle for the connection probabilities, is assessed, and efficient approximation algorithms for both problems and some useful variants are developed.
Tight bounds on information dissemination in sparse mobile networks
TLDR
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 <i>k</i>.
Mining top-K frequent itemsets through progressive sampling
TLDR
A number of experiments conducted on both synthetic and real benchmark datasets show that using samples substantially smaller than the original dataset enable to approximate the actual top-K frequent itemsets with accuracy much higher than what analytically proved.
On the Space and Access Complexity of Computation DAGs
TLDR
A unifying framework for proving lower bounds on the space complexity ofCDAGs is presented, which captures most of the bounds known in the literature for relevant CDAGs, previously proved through ad-hoc arguments.
Network-Oblivious Algorithms
TLDR
This work proposes a framework for network-obliviousness based on a model of computation where the only parameter is the problem's input size, and shows that optimality in the latter model implies Optimality in a block-variant of the decomposable BSP model, which effectively describes a wide and significant class of parallel platforms.
MADMX: A Novel Strategy for Maximal Dense Motif Extraction
TLDR
Density, a simple and flexible measure for bounding the number of don't cares in a motif, is introduced, defined as the ratio of solid (i.e., different from don't care) characters to the total length of the motif.
Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially
TLDR
This paper presents coreset-based 2-round MapReduce algorithms for center-based clustering, and shows that the algorithms become very space-efficient for the important case of small (constant) D .
A practical constructive scheme for deterministic shared-memory access
TLDR
An access protocol is provided so that the processors can read/write any set of distinct data in $O((N'')^{1/3} N''+\log N)$ worst-case time.
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