26,127 research outputs found
Comparison of distributed computing approaches to complexity of n-gram extraction
In this paper we compare different technologies that support distributed computing as a means to address complex tasks. We address the task of n-gram text extraction which is a big computational given a large amount of textual data to process. In order to deal with such complexity we have to adopt and implement parallelization patterns. Nowadays there are several patterns, platforms and even languages that can be used for the parallelization task. We implemented this task on three platforms: (1) MPJ Express, (2) Apache Hadoop, and (3) Apache Spark. The experiments were implemented using two kinds of datasets composed by: (A) a large number of small files, and (B) a small number of large files. Each experiment uses both datasets and the experiment repeats for a set of different file sizes. We compared performance and efficiency among MPJ Express, Apache Hadoop and Apache Spark. As a final result we are able to provide guidelines for choosing the platform that is best suited for each kind of data set regarding its overall size and granularity of the input data.info:eu-repo/semantics/publishedVersio
Comparison of distributed computing approaches to complexity of n-gram extraction
In this paper we compare different technologies that support distributed computing as a means to address complex tasks. We address the task of n-gram text extraction which is a big computational given a large amount of textual data to process. In order to deal with such complexity we have to adopt and implement parallelization patterns. Nowadays there are several patterns, platforms and even languages that can be used for the parallelization task. We implemented this task on three platforms: (1) MPJ Express, (2) Apache Hadoop, and (3) Apache Spark. The experiments were implemented using two kinds of datasets composed by: (A) a large number of small files, and (B) a small number of large files. Each experiment uses both datasets and the experiment repeats for a set of different file sizes. We compared performance and efficiency among MPJ Express, Apache Hadoop and Apache Spark. As a final result we are able to provide guidelines for choosing the platform that is best suited for each kind of data set regarding its overall size and granularity of the input data.info:eu-repo/semantics/publishedVersio
A Review on Software Architectures for Heterogeneous Platforms
The increasing demands for computing performance have been a reality
regardless of the requirements for smaller and more energy efficient devices.
Throughout the years, the strategy adopted by industry was to increase the
robustness of a single processor by increasing its clock frequency and mounting
more transistors so more calculations could be executed. However, it is known
that the physical limits of such processors are being reached, and one way to
fulfill such increasing computing demands has been to adopt a strategy based on
heterogeneous computing, i.e., using a heterogeneous platform containing more
than one type of processor. This way, different types of tasks can be executed
by processors that are specialized in them. Heterogeneous computing, however,
poses a number of challenges to software engineering, especially in the
architecture and deployment phases. In this paper, we conduct an empirical
study that aims at discovering the state-of-the-art in software architecture
for heterogeneous computing, with focus on deployment. We conduct a systematic
mapping study that retrieved 28 studies, which were critically assessed to
obtain an overview of the research field. We identified gaps and trends that
can be used by both researchers and practitioners as guides to further
investigate the topic
struc2vec: Learning Node Representations from Structural Identity
Structural identity is a concept of symmetry in which network nodes are
identified according to the network structure and their relationship to other
nodes. Structural identity has been studied in theory and practice over the
past decades, but only recently has it been addressed with representational
learning techniques. This work presents struc2vec, a novel and flexible
framework for learning latent representations for the structural identity of
nodes. struc2vec uses a hierarchy to measure node similarity at different
scales, and constructs a multilayer graph to encode structural similarities and
generate structural context for nodes. Numerical experiments indicate that
state-of-the-art techniques for learning node representations fail in capturing
stronger notions of structural identity, while struc2vec exhibits much superior
performance in this task, as it overcomes limitations of prior approaches. As a
consequence, numerical experiments indicate that struc2vec improves performance
on classification tasks that depend more on structural identity.Comment: 10 pages, KDD2017, Research Trac
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