56 research outputs found
A Hybrid Multicast-Unicast Infrastructure for Efficient Publish-Subscribe in Enterprise Networks
One of the main challenges in building a large scale publish-subscribe
infrastructure in an enterprise network, is to provide the subscribers with the
required information, while minimizing the consumed host and network resources.
Typically, previous approaches utilize either IP multicast or point-to-point
unicast for efficient dissemination of the information.
In this work, we propose a novel hybrid framework, which is a combination of
both multicast and unicast data dissemination. Our hybrid framework allows us
to take the advantages of both multicast and unicast, while avoiding their
drawbacks. We investigate several algorithms for computing the best mapping of
publishers' transmissions into multicast and unicast transport.
Using extensive simulations, we show that our hybrid framework reduces
consumed host and network resources, outperforming traditional solutions. To
insure the subscribers interests closely resemble those of real-world settings,
our simulations are based on stock market data and on recorded IBM WebShpere
subscriptions
Accelerating binary biclustering on platforms with CUDA-enabled GPUs
© 2018 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Information Sciences. The Version of Record is available online at https://doi.org/10.1016/j.ins.2018.05.025This is a version of: J. González-DomĂnguez and R. R. ExpĂłsito, "Accelerating binary biclustering on platforms with CUDA-enabled GPUs", Information Sciences, Vol. 496, Sept. 2019, pp. 317-325, https://doi.org/10.1016/j.ins.2018.05.025[Abstract]: Data mining is nowadays essential in many scientific fields to extract valuable information from large input datasets and transform it into an understandable structure. For instance, biclustering techniques are very useful in identifying subsets of two-dimensional data where both rows and columns are correlated. However, some biclustering techniques have become extremely time-consuming when processing very large datasets, which nowadays prevents their use in many areas of research and industry (such as bioinformatics) that have experienced an explosive growth on the amount of available data. In this work we present CUBiBit, a tool that accelerates the search for relevant biclusters on binary data by exploiting the computational capabilities of CUDA-enabled GPUs as well as the several CPU cores available in most current systems. The experimental evaluation has shown that CUBiBit is up to 116 times faster than the fastest state-of-the-art tool, BiBit, in a system with two Intel Sandy Bridge processors (16 CPU cores) and three NVIDIA K20 GPUs. CUBiBit is publicly available to download from https://sourceforge.net/projects/cubibitThis work was supported by the Ministry of Economy, Industry and Competitiveness of Spain and FEDER funds of the European Union [grant TIN2016-75845-P (AEI/FEDER/UE)], as well as by Xunta de Galicia (Centro Singular de Investigacion de Galicia accreditation 2016-2019, ref. EDG431G/01).Xunta de Galicia; EDG431G/0
BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data
The BayesBinMix package offers a Bayesian framework for clustering binary
data with or without missing values by fitting mixtures of multivariate
Bernoulli distributions with an unknown number of components. It allows the
joint estimation of the number of clusters and model parameters using Markov
chain Monte Carlo sampling. Heated chains are run in parallel and accelerate
the convergence to the target posterior distribution. Identifiability issues
are addressed by implementing label switching algorithms. The package is
demonstrated and benchmarked against the Expectation-Maximization algorithm
using a simulation study as well as a real dataset.Comment: Accepted to the R Journal. The package is available on CRAN:
https://CRAN.R-project.org/package=BayesBinMi
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