Clustering is a useful technique that divides data points into groups, also known as clusters, such that the data points of the same cluster exhibit similar properties. Typical clustering algorithms assign each data point to at least one cluster. However, in practical datasets like microarray gene dataset, only a subset of the genes are highly correlated and the dataset is often polluted with a huge volume of genes that are irrelevant. In such cases, it is important to ignore the poorly correlated genes and just cluster the highly correlated genes. Automated Hierarchical Density Shaving (Auto-HDS) is a non-parametric density based technique that partitions only the relevant subset of the dataset into multiple clusters while pruning the rest. Auto-HDS performs a hierarchical clustering that identifies dense clusters of different densities and finds a compact hierarchy of the clusters identified. Some of the key features of Auto-HDS include selection and ranking of clusters using custom stability criterion and a topologically meaningful 2D projection and visualization of the clusters discovered in the higher dimensional original space. However, a key limitation of Auto-HDS is that it requires O(n*n) storage, and O(n*n*logn) computational complexity, making it scale up to only a few 10s of thousands of points. In this thesis, two extensions to Auto-HDS are presented for lower dimensional datasets that can generate clustering identical to Auto-HDS but can scale to much larger datasets. We first introduce Partitioned Auto-HDS that provides significant reduction in time and space complexity and makes it possible to generate the Auto-HDS cluster hierarchy on much larger datasets with 100s of millions of data points. Then, we describe Parallel Auto-HDS that takes advantage of the inherent parallelism available in Partitioned Auto-HDS to scale to even larger datasets without a corresponding increase in actual run time when a group of processors are available for parallel execution. Partitioned Auto-HDS is implemented on top of GeneDIVER, a previously existing Java based streaming implementation of Auto-HDS, and thus it retains all the key features of Auto-HDS including ranking, automatic selection of clusters and 2D visualization of the discovered cluster topology. / text

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. In this thesis distributed computing paradigms have been investigated to speed up SVM training, by partitioning a large training dataset into small data chunks and process each chunk in parallel utilizing the resources of a cluster of computers. A resource aware parallel SVM algorithm is introduced for large scale image annotation in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of the algorithm in heterogeneous computing environments. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. A resource aware parallel multiclass SVM algorithm for large scale image annotation in parallel using a cluster of computers is introduced. The combination of classifiers leads to substantial reduction of classification error in a wide range of applications. Among them SVM ensembles with bagging is shown to outperform a single SVM in terms of classification accuracy. However, SVM ensembles training are notably a computationally intensive process especially when the number replicated samples based on bootstrapping is large. A distributed SVM ensemble algorithm for image annotation is introduced which re-samples the training data based on bootstrapping and training SVM on each sample in parallel using a cluster of computers. The above algorithms are evaluated in both experimental and simulation environments showing that the distributed SVM algorithm, distributed multiclass SVM algorithm, and distributed SVM ensemble algorithm, reduces the training time significantly while maintaining a high level of accuracy in classifications.

The thesis evaluates the viability of reducing power consumption of a contem- porary computer cluster by using more power-efficient hardware components. The cluster in question runs an Map-Reduce algorithm implementation and the worker nodes consist of either systems with an ARM CPU or systems which combine both an ARM CPU and an FPGA in a single package. The behavior of such cluster is discussed from both performance side as well as power consumption side. The text discusses the problems and peculiarities with the integration of an ARM-based and especially the combined ARM-FPGA-based systems into the Map-Reduce framework. The Map-Reduce framework performance itself is eval- uated to identify the gravest performance bottlenecks when using the framework in the environment with ARM systems. 1

<div>In Divide & Recombine (D&R), data are divided into subsets, analytic methodsare applied to each subset independently, with no communication between processes;then the subset outputs for each method are recombined. For big data, this providesalmost all of the analytic tasking needed when data are analyzed. It also provideshigh computational performance because typically most of the computation is em-barrassingly parallel, the simplest parallel computation.</div><div><br></div><div>Another kind of tasking must address computational performance and numericaccuracy: the computing of functions of all of the data, or “statistics”. For data bigand small, it is often important to compute such statistics for all of the data, whichcan be summaries of the data, such as sample quantiles of continuous variables, orcan process the data into a form that helps analysis, such as dividing the data intorepresentative subsets. Development of computational methods to compute thesestatistics can be challenging.</div><div><br></div><div>D&R can be a very effective framework for computing statistics. To supportthis, we introduce the concept of embarrassingly parallel (EP) statistics, both weakand strong. The concept of EP statistics is not entirely new, but has had littledevelopment. The existing methodology is mainly sums of sums. For example, this isdone when computing the necessary statistics for least squares where sums of productsand cross productions are carried out on subsets then summed across subsets. Ourtreatment of EP statistics has taken the concept much further. The outcome is abilityto use EP statistics in conjunction with the use a Fourier series to approximate an optimization criteria. The series terms, which are strongly EP statistics, are summedacross subsets, and the result is optimized. These are EP-F computational methods.</div><div><br></div><div>We have so far developed two EP-F computational methods for two widely usedstatistic computations. EP-F-Quantile is for quantiles of big data, and EP-F-KDtreeis for KD-trees. Speed and accuracy of EPF-Quantile are compared with that of thewell-known binning method, which also can be formulated in terms of EP statistics. EPF-KDtree is the first parallel KD-tree computational method of which we areaware. EP and EPF computational methods have potentially many other applicationsto computing statistics.</div>

<p>The work aims to clarify what nautical officers onboard in today's merchant can do to help reduce bunker consumption during the voyage? The question we have asked ourselves during the autumn term in 2008 much was said about the premium bunker prices, and how the future could affect our daily lives as nautical officer. We felt here that the school had relatively little knowledge of the subject and therefore felt that it would be interesting to identify how it really looks like onboard the ships today. To collect information we contacted 7 Swedish companies that operate with different types of vessels, in order to get a broad picture of the whole industry. For shipping companies we asked questions about how actively they were working on the issue and what methods they used. We found that the owners worked with the issue but that it so far was a little on the go. All but one company in the survey provided the vessels are instructed to run bunker efficiently.</p> / <p>Arbetet syftar till att klarlägga vad nautikerna ombord i dagens handelsfartyg kan göra för att reducera bunkerförbrukningen under pågående sjöresa? Frågan ställde vi oss eftersom det under höstterminen 2008 talades mycket om de ”skyhöga” bunkerpriserna och hur de i framtiden skulle påverka vår vardag som nautiker. Vi kände att det på skolan fanns förhållandevis lite kunskap i ämnet och tyckte därför att det vore intressant att kartlägga hur det verkligen ser ut ombord i fartygen idag. Främst gällande direktiv till befälen rörande bunkerreducerande metoder.</p><p>För att få underlag för arbetet kontaktade vi 7 stycken svenska rederier som är verksamma med olika typer av fartyg, detta för att få en bred bild av hela sjöfartsbranschen. Till rederierna ställde vi frågor om de aktivt arbetade med frågan och i så fall vad de använde sig av för metoder. Vi kunde konstatera att redarna arbetade med frågan men att det än så länge låg lite i startgroparna. Alla utom ett rederi i undersökning gav fartygen instruktioner om att köra bunkereffektivt.</p>

The work aims to clarify what nautical officers onboard in today's merchant can do to help reduce bunker consumption during the voyage? The question we have asked ourselves during the autumn term in 2008 much was said about the premium bunker prices, and how the future could affect our daily lives as nautical officer. We felt here that the school had relatively little knowledge of the subject and therefore felt that it would be interesting to identify how it really looks like onboard the ships today. To collect information we contacted 7 Swedish companies that operate with different types of vessels, in order to get a broad picture of the whole industry. For shipping companies we asked questions about how actively they were working on the issue and what methods they used. We found that the owners worked with the issue but that it so far was a little on the go. All but one company in the survey provided the vessels are instructed to run bunker efficiently. / Arbetet syftar till att klarlägga vad nautikerna ombord i dagens handelsfartyg kan göra för att reducera bunkerförbrukningen under pågående sjöresa? Frågan ställde vi oss eftersom det under höstterminen 2008 talades mycket om de ”skyhöga” bunkerpriserna och hur de i framtiden skulle påverka vår vardag som nautiker. Vi kände att det på skolan fanns förhållandevis lite kunskap i ämnet och tyckte därför att det vore intressant att kartlägga hur det verkligen ser ut ombord i fartygen idag. Främst gällande direktiv till befälen rörande bunkerreducerande metoder. För att få underlag för arbetet kontaktade vi 7 stycken svenska rederier som är verksamma med olika typer av fartyg, detta för att få en bred bild av hela sjöfartsbranschen. Till rederierna ställde vi frågor om de aktivt arbetade med frågan och i så fall vad de använde sig av för metoder. Vi kunde konstatera att redarna arbetade med frågan men att det än så länge låg lite i startgroparna. Alla utom ett rederi i undersökning gav fartygen instruktioner om att köra bunkereffektivt.

This thesis describes the implementation of a UNIX-styled shell environment for cloud systems. This new scripting language, the cloud shell (CLSH), uses a syntax based upon the familiar BASH shell of UNIX systems. This familiar syntax allows users to quickly learn the new environment. The difference, as compared to BASH, is that CLSH gives the user easy access to the parallelism of the cloud. Indeed, the user does not need to explicitly refer to the cloud at all; the cloud becomes simply a virtual file system and the user experience is quite similar to standard bash programming. This cloud shell is built into Hadoop¡¦s HDFS file system. The difference, as compared to HDFS, is that CLSH offers a full range of UNIX-style commands, rather than a small subset of simple commands. Moreover, CLSH is a full-fledged scripting language that offers much more control over file management than does HDFS. To achieve comparable behavior within HDFS, the user must use either the Pig Latin tool or else use java scripting. Not only are these alternatives harder to use than CLSH, but they also perform slower and are incapable of performing certain tasks that CLSH can easily achieve. Moreover, the cloud shell environment simply provides the user with a better cloud interface; it does not preclude the use of Pig Latin or Java scripts.

Made available in DSpace on 2014-10-09T12:42:28Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:02:08Z (GMT). No. of bitstreams: 0 / Dissertação (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Made available in DSpace on 2014-10-09T12:42:28Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:02:08Z (GMT). No. of bitstreams: 0 / O presente estudo propõe uma alternativa para tratamento químico da urina para redução no consumo de água em descargas residenciais, possibilitando um maior tempo de permanência da água a ser descartada na bacia sanitária antes do acionamento da descarga. O processo consiste em neutralizar os componentes responsáveis pelo odor e cor característicos da urina a partir da reação química com dicloroisocianurato de sódio, NaDCC. O composto também apresenta ação bactericida podendo agir por um determinado período de tempo. Além disso, considera-se a adição de um componente indicador de nível de saturação do meio para otimizar o efeito sobre os aspectos estéticos e sanitários (odor, cor e presença de bactérias). O tratamento proposto deverá apresentar baixo custo estimulando a mudança de paradigmas por meio da conscientização da importância da redução do consumo de água nas residências. Pretende-se acompanhar o desempenho do processo proposto a partir de ensaios físico-químicos e microbiológicos. / Dissertação (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Healthcare managers who are unaware of the various strategies that exist for reducing turnover could adversely affect patient care, organizational morale and performance, and the achievement of organizational goals. The purpose of this qualitative multiple case study was to explore strategies healthcare supervisors used to reduce employee turnover. The participants comprised 3 senior healthcare managers located in central Texas responsible for hiring, firing, training, supervising, and successfully using strategies to reduce employee turnover. Herzberg's motivation-hygiene theory provided the conceptual framework. Data were collected from semistructured interviews and a review of company documents. Thematic analysis of the data resulted in 5 emergent themes: peer-to-peer feedback, valuing employees, rewards and incentives, opportunities for growth, and training programs. The results of this study might contribute to social change by enhancing healthcare managers' understanding of the strategies that can be used to reduce employee turnover and improve existing conditions among patients, their families, staff, communities, and organizations.