21 research outputs found
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MapReduce network enabled algorithms for classification based on association rules
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters.
The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions.
In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches.
Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization
An optimized Speculative Execution Strategy Based on Local Data Prediction in Heterogeneous Hadoop Environment
Hadoop is a famous parallel computing framework that is applied to process large-scale data, but there exists such a task in hadoop framework, which is called “Straggling task” and has a serious impact on Hadoop. Speculative execution (SE) is an effective way to deal with the “Straggling task” by monitoring the real-time rate of running tasks and back up the “Straggler” on another node to increase the opportunity of completing backup task ahead of original. There are many problems in the proposed SE strategies, such as “Straggling task” misjudgment, improper selection of backup nodes, which will result in inefficient implementation of SE. In this paper, we propose an optimized SE strategy based on local data prediction, it collects task execution information in real time and uses Local regression to predict remaining time of the current task, and selects the appropriate backup task node according to the actual requirements, at the same time, it uses the consumption and benefit model to maximizes the effectiveness of SE. Finally, the strategy is implemented in Hadoop-2.6.0, the experiment proves that the optimized strategy not only enhances the accuracy of selecting the “Straggler” task candidates, but also shows better performance in heterogeneous Hadoop environment
An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes
Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes. “Straggling” tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient method of processing “Straggling” Tasks by monitoring real-time running status of tasks and then selectively backing up “Stragglers” in another node to increase the chance to complete the entire mission early. Present speculative execution strategies meet challenges on misjudgement of “Straggling” tasks and improper selection of backup nodes, which leads to inefficient implementation of speculative executive processes. This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) by introducing non-cooperative game schemes. The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem, where the tasks are regarded as game participants, whilst total task execution time of the entire cluster as the utility function. In that case, the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point, i.e. the final resource scheduling scheme to be obtained. The strategy has been implemented in Hadoop-2.x. Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load, Busy Load and Busy Load with Skewed Data
Hadoop-Oriented SVM-LRU (H-SVM-LRU): An Intelligent Cache Replacement Algorithm to Improve MapReduce Performance
Modern applications can generate a large amount of data from different
sources with high velocity, a combination that is difficult to store and
process via traditional tools. Hadoop is one framework that is used for the
parallel processing of a large amount of data in a distributed environment,
however, various challenges can lead to poor performance. Two particular issues
that can limit performance are the high access time for I/O operations and the
recomputation of intermediate data. The combination of these two issues can
result in resource wastage. In recent years, there have been attempts to
overcome these problems by using caching mechanisms. Due to cache space
limitations, it is crucial to use this space efficiently and avoid cache
pollution (the cache contains data that is not used in the future). We propose
Hadoop-oriented SVM-LRU (HSVM- LRU) to improve Hadoop performance. For this
purpose, we use an intelligent cache replacement algorithm, SVM-LRU, that
combines the well-known LRU mechanism with a machine learning algorithm, SVM,
to classify cached data into two groups based on their future usage.
Experimental results show a significant decrease in execution time as a result
of an increased cache hit ratio, leading to a positive impact on Hadoop
performance
Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment
Distributed Computing has achieved tremendous development since cloud computing was proposed in 2006, and played a vital role promoting rapid growth of data collecting and analysis models, e.g., Internet of things, Cyber-Physical Systems, Big Data Analytics, etc. Hadoop has become a data convergence platform for sensor networks. As one of the core components, MapReduce facilitates allocating, processing and mining of collected large-scale data, where speculative execution strategies help solve straggler problems. However, there is still no efficient solution for accurate estimation on execution time of run-time tasks, which can affect task allocation and distribution in MapReduce. In this paper, task execution data have been collected and employed for the estimation. A two-phase regression (TPR) method is proposed to predict the finishing time of each task accurately. Detailed data of each task have drawn interests with detailed analysis report being made. According to the results, the prediction accuracy of concurrent tasks’ execution time can be improved, in particular for some regular jobs
MapReduce network enabled algorithms for classification based on association rules
There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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MapReduce based RDF assisted distributed SVM for high throughput spam filtering
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses.
Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart.
Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure.
The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin
New scalable machine learning methods: beyond classification and regression
Programa Oficial de Doutoramento en Computación . 5009V01[Abstract]
The recent surge in data available has spawned a new and promising age of machine
learning. Success cases of machine learning are arriving at an increasing rate as some
algorithms are able to leverage immense amounts of data to produce great complicated
predictions. Still, many algorithms in the toolbox of the machine learning practitioner
have been render useless in this new scenario due to the complications associated with
large-scale learning. Handling large datasets entails logistical problems, limits the computational
and spatial complexity of the used algorithms, favours methods with few or
no hyperparameters to be con gured and exhibits speci c characteristics that complicate
learning. This thesis is centered on the scalability of machine learning algorithms,
that is, their capacity to maintain their e ectivity as the scale of the data grows, and
how it can be improved. We focus on problems for which the existing solutions struggle
when the scale grows. Therefore, we skip classi cation and regression problems and
focus on feature selection, anomaly detection, graph construction and explainable machine
learning. We analyze four di erent strategies to obtain scalable algorithms. First,
we explore distributed computation, which is used in all of the presented algorithms.
Besides this technique, we also examine the use of approximate models to speed up
computations, the design of new models that take advantage of a characteristic of the
input data to simplify training and the enhancement of simple models to enable them
to manage large-scale learning. We have implemented four new algorithms and six
versions of existing ones that tackle the mentioned problems and for each one we report
experimental results that show both their validity in comparison with competing
methods and their capacity to scale to large datasets. All the presented algorithms
have been made available for download and are being published in journals to enable
practitioners and researchers to use them.[Resumen]
El reciente aumento de la cantidad de datos disponibles ha dado lugar a una nueva y
prometedora era del aprendizaje máquina. Los éxitos en este campo se están sucediendo
a un ritmo cada vez mayor gracias a la capacidad de algunos algoritmos de aprovechar
inmensas cantidades de datos para producir predicciones difíciles y muy certeras. Sin
embargo, muchos de los algoritmos hasta ahora disponibles para los científicos de datos
han perdido su efectividad en este nuevo escenario debido a las complicaciones asociadas
al aprendizaje a gran escala. Trabajar con grandes conjuntos de datos conlleva
problemas logísticos, limita la complejidad computacional y espacial de los algoritmos
utilizados, favorece los métodos con pocos o ningún hiperparámetro a configurar y
muestra complicaciones específicas que dificultan el aprendizaje. Esta tesis se centra en
la escalabilidad de los algoritmos de aprendizaje máquina, es decir, en su capacidad de
mantener su efectividad a medida que la escala del conjunto de datos aumenta. Ponemos
el foco en problemas cuyas soluciones actuales tienen problemas al aumentar la
escala. Por tanto, obviando la clasificación y la regresión, nos centramos en la selección
de características, detección de anomalías, construcción de grafos y en el aprendizaje
máquina explicable. Analizamos cuatro estrategias diferentes para obtener algoritmos
escalables. En primer lugar, exploramos la computación distribuida, que es utilizada en
todos los algoritmos presentados. Además de esta técnica, también examinamos el uso
de modelos aproximados para acelerar los cálculos, el dise~no de modelos que aprovechan
una particularidad de los datos de entrada para simplificar el entrenamiento y la
potenciación de modelos simples para adecuarlos al aprendizaje a gran escala. Hemos
implementado cuatro nuevos algoritmos y seis versiones de algoritmos existentes que
tratan los problemas mencionados y para cada uno de ellos detallamos resultados experimentales
que muestran tanto su validez en comparación con los métodos previamente
disponibles como su capacidad para escalar a grandes conjuntos de datos. Todos los algoritmos presentados han sido puestos a disposición del lector para su descarga y
se han difundido mediante publicaciones en revistas científicas para facilitar que tanto
investigadores como científicos de datos puedan conocerlos y utilizarlos.[Resumo]
O recente aumento na cantidade de datos dispo~nibles deu lugar a unha nova e prometedora
era no aprendizaxe máquina. Os éxitos neste eido estanse a suceder a un
ritmo cada vez maior gracias a capacidade dalgúns algoritmos de aproveitar inmensas
cantidades de datos para producir prediccións difíciles e moi acertadas. Non obstante,
moitos dos algoritmos ata agora dispo~nibles para os científicos de datos perderon a súa
efectividade neste novo escenario por mor das complicacións asociadas ao aprendizaxe
a grande escala. Traballar con grandes conxuntos de datos leva consigo problemas
loxísticos, limita a complexidade computacional e espacial dos algoritmos empregados,
favorece os métodos con poucos ou ningún hiperparámetro a configurar e ten complicacións específicas que dificultan o aprendizaxe. Esta tese céntrase na escalabilidade dos
algoritmos de aprendizaxe máquina, é dicir, na súa capacidade de manter a súa efectividade
a medida que a escala do conxunto de datos aumenta. Tratamos problemas para
os que as solucións dispoñibles teñen problemas cando crece a escala. Polo tanto, deixando
no canto a clasificación e a regresión, centrámonos na selección de características,
detección de anomalías, construcción de grafos e no aprendizaxe máquina explicable.
Analizamos catro estratexias diferentes para obter algoritmos escalables. En primeiro
lugar, exploramos a computación distribuída, que empregamos en tódolos algoritmos
presentados. Ademáis desta técnica, tamén examinamos o uso de modelos aproximados
para acelerar os cálculos, o deseño de modelos que aproveitan unha particularidade dos
datos de entrada para simplificar o adestramento e a potenciación de modelos sinxelos
para axeitalos ao aprendizaxe a gran escala. Implementamos catro novos algoritmos e
seis versións de algoritmos existentes que tratan os problemas mencionados e para cada
un deles expoñemos resultados experimentais que mostran tanto a súa validez en comparación cos métodos previamente dispoñibles como a súa capacidade para escalar a
grandes conxuntos de datos. Tódolos algoritmos presentados foron postos a disposición
do lector para a súa descarga e difundíronse mediante publicacións en revistas científicas para facilitar que tanto investigadores como científicos de datos poidan coñecelos e
empregalos
Distributed multi-label learning on Apache Spark
This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art