2,464 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
Balancing the Communication Load of Asynchronously Parallelized Machine Learning Algorithms
Stochastic Gradient Descent (SGD) is the standard numerical method used to
solve the core optimization problem for the vast majority of machine learning
(ML) algorithms. In the context of large scale learning, as utilized by many
Big Data applications, efficient parallelization of SGD is in the focus of
active research. Recently, we were able to show that the asynchronous
communication paradigm can be applied to achieve a fast and scalable
parallelization of SGD. Asynchronous Stochastic Gradient Descent (ASGD)
outperforms other, mostly MapReduce based, parallel algorithms solving large
scale machine learning problems. In this paper, we investigate the impact of
asynchronous communication frequency and message size on the performance of
ASGD applied to large scale ML on HTC cluster and cloud environments. We
introduce a novel algorithm for the automatic balancing of the asynchronous
communication load, which allows to adapt ASGD to changing network bandwidths
and latencies.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0495
Tupleware: Redefining Modern Analytics
There is a fundamental discrepancy between the targeted and actual users of
current analytics frameworks. Most systems are designed for the data and
infrastructure of the Googles and Facebooks of the world---petabytes of data
distributed across large cloud deployments consisting of thousands of cheap
commodity machines. Yet, the vast majority of users operate clusters ranging
from a few to a few dozen nodes, analyze relatively small datasets of up to a
few terabytes, and perform primarily compute-intensive operations. Targeting
these users fundamentally changes the way we should build analytics systems.
This paper describes the design of Tupleware, a new system specifically aimed
at the challenges faced by the typical user. Tupleware's architecture brings
together ideas from the database, compiler, and programming languages
communities to create a powerful end-to-end solution for data analysis. We
propose novel techniques that consider the data, computations, and hardware
together to achieve maximum performance on a case-by-case basis. Our
experimental evaluation quantifies the impact of our novel techniques and shows
orders of magnitude performance improvement over alternative systems
Finding Top-k Dominance on Incomplete Big Data Using Map-Reduce Framework
Incomplete data is one major kind of multi-dimensional dataset that has random-distributed missing nodes in its dimensions. It is very difficult to retrieve information from this type of dataset when it becomes huge. Finding top-k dominant values in this type of dataset is a challenging procedure. Some algorithms are present to enhance this process but are mostly efficient only when dealing with a small-size incomplete data. One of the algorithms that make the application of TKD query possible is the Bitmap Index Guided (BIG) algorithm. This algorithm strongly improves the performance for incomplete data, but it is not originally capable of finding top-k dominant values in incomplete big data, nor is it designed to do so. Several other algorithms have been proposed to find the TKD query, such as Skyband Based and Upper Bound Based algorithms, but their performance is also questionable. Algorithms developed previously were among the first attempts to apply TKD query on incomplete data; however, all these had weak performances or were not compatible with the incomplete data. This thesis proposes MapReduced Enhanced Bitmap Index Guided Algorithm (MRBIG) for dealing with the aforementioned issues. MRBIG uses the MapReduce framework to enhance the performance of applying top-k dominance queries on huge incomplete datasets. The proposed approach uses the MapReduce parallel computing approach using multiple computing nodes. The framework separates the tasks between several computing nodes that independently and simultaneously work to find the result. This method has achieved up to two times faster processing time in finding the TKD query result in comparison to previously presented algorithms
Efficient Processing of k Nearest Neighbor Joins using MapReduce
k nearest neighbor join (kNN join), designed to find k nearest neighbors from
a dataset S for every object in another dataset R, is a primitive operation
widely adopted by many data mining applications. As a combination of the k
nearest neighbor query and the join operation, kNN join is an expensive
operation. Given the increasing volume of data, it is difficult to perform a
kNN join on a centralized machine efficiently. In this paper, we investigate
how to perform kNN join using MapReduce which is a well-accepted framework for
data-intensive applications over clusters of computers. In brief, the mappers
cluster objects into groups; the reducers perform the kNN join on each group of
objects separately. We design an effective mapping mechanism that exploits
pruning rules for distance filtering, and hence reduces both the shuffling and
computational costs. To reduce the shuffling cost, we propose two approximate
algorithms to minimize the number of replicas. Extensive experiments on our
in-house cluster demonstrate that our proposed methods are efficient, robust
and scalable.Comment: VLDB201
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