2,507 research outputs found
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
Distributed Holistic Clustering on Linked Data
Link discovery is an active field of research to support data integration in
the Web of Data. Due to the huge size and number of available data sources,
efficient and effective link discovery is a very challenging task. Common
pairwise link discovery approaches do not scale to many sources with very large
entity sets. We here propose a distributed holistic approach to link many data
sources based on a clustering of entities that represent the same real-world
object. Our clustering approach provides a compact and fused representation of
entities, and can identify errors in existing links as well as many new links.
We support a distributed execution of the clustering approach to achieve faster
execution times and scalability for large real-world data sets. We provide a
novel gold standard for multi-source clustering, and evaluate our methods with
respect to effectiveness and efficiency for large data sets from the geographic
and music domains
Garbage collection auto-tuning for Java MapReduce on Multi-Cores
MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ, a MapReduce Java framework for multi-core architectures. We evaluate its scalability on a four-core, hyperthreaded Intel Core i7 processor, using a set of standard MapReduce benchmarks. We investigate the significant impact that Java runtime garbage collection has on the performance and scalability of MRJ. We propose the use of memory management auto-tuning techniques based on machine learning. With our auto-tuning approach, we are able to achieve MRJ performance within 10% of optimal on 75% of our benchmark tests
A Framework for Genetic Algorithms Based on Hadoop
Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in
many real-world applications. The sequential execution of GAs requires
considerable computational power both in time and resources. Nevertheless, GAs
are naturally parallel and accessing a parallel platform such as Cloud is easy
and cheap. Apache Hadoop is one of the common services that can be used for
parallel applications. However, using Hadoop to develop a parallel version of
GAs is not simple without facing its inner workings. Even though some
sequential frameworks for GAs already exist, there is no framework supporting
the development of GA applications that can be executed in parallel. In this
paper is described a framework for parallel GAs on the Hadoop platform,
following the paradigm of MapReduce. The main purpose of this framework is to
allow the user to focus on the aspects of GA that are specific to the problem
to be addressed, being sure that this task is going to be correctly executed on
the Cloud with a good performance. The framework has been also exploited to
develop an application for Feature Subset Selection problem. A preliminary
analysis of the performance of the developed GA application has been performed
using three datasets and shown very promising performance
Performance Evaluation of an Independent Time Optimized Infrastructure for Big Data Analytics that Maintains Symmetry
Traditional data analytics tools are designed to deal with the asymmetrical type of data i.e., structured, semi-structured, and unstructured. The diverse behavior of data produced by different sources requires the selection of suitable tools. The restriction of recourses to deal with a huge volume of data is a challenge for these tools, which affects the performances of the tool's execution time. Therefore, in the present paper, we proposed a time optimization model, shares common HDFS (Hadoop Distributed File System) between three Name-node (Master Node), three Data-node, and one Client-node. These nodes work under the DeMilitarized zone (DMZ) to maintain symmetry. Machine learning jobs are explored from an independent platform to realize this model. In the first node (Name-node 1), Mahout is installed with all machine learning libraries through the maven repositories. The second node (Name-node 2), R connected to Hadoop, is running through the shiny-server. Splunk is configured in the third node (Name-node 3) and is used to analyze the logs. Experiments are performed between the proposed and legacy model to evaluate the response time, execution time, and throughput. K-means clustering, Navies Bayes, and recommender algorithms are run on three different data sets, i.e., movie rating, newsgroup, and Spam SMS data set, representing structured, semi-structured, and unstructured data, respectively. The selection of tools defines data independence, e.g., Newsgroup data set to run on Mahout as others cannot be compatible with this data. It is evident from the outcome of the data that the performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model. In addition, the proposed model can process any kind of algorithm on different sets of data, which resides in its native formats
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