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Milton, Robert. "Time-series in distributed real-time databases." Thesis, University of Skövde, Department of Computer Science, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-827.
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<p>In a distributed real-time environment where it is imperative to make correct decisions it is important to have all facts available to make the most accurate decision in a certain situation. An example of such an environment is an Unmanned Aerial Vehicle (UAV) system where several UAVs cooperate to carry out a certain task and the data recorded is analyzed after the completion of the mission. This project aims to define and implement a time series architecture for use together with a distributed real-time database for the ability to store temporal data. The result from this project is a time series (TS) architecture that uses DeeDS, a distributed real-time database, for storage. The TS architecture is used by an application modelled from a UAV scenario for storing temporal data. The temporal data is produced by a simulator. The TS architecture solves the problem of storing temporal data for applications using DeeDS. The TS architecture is also useful as a foundation for integrating time series in DeeDS since it is designed for space efficiency and real-time requirements.</p>
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Charapko, Aleksey. "Time Series Similarity Search in Distributed Key-Value Data Stores Using R-Trees." UNF Digital Commons, 2015. http://digitalcommons.unf.edu/etd/565.
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Time series data are sequences of data points collected at certain time intervals. The advance in mobile and sensor technologies has led to rapid growth in the available amount of time series data. The ability to search large time series data sets can be extremely useful in many applications. In healthcare, a system monitoring vital signals can perform a search against the past data and identify possible health threatening conditions. In engineering, a system can analyze performances of complicated equipment and identify possible failure situations or needs of maintenance based on historical data.
Existing search methods for time series data are limited in many ways. Systems utilizing memory-bound or disk-bound indexes are restricted by the resources of a single machine or hard drive. Systems that do not use indexes must search through the entire database whenever a search is requested.
The proposed system uses multidimensional index in the distributed storage environment to break the bound of one physical machine and allow for high data scalability. Utilizing an index allows the system to locate the patterns similar to the query without having to examine the entire dataset, which can significantly reduce the amount of computing resources required. The system uses an Apache HBase distributed key-value database to store the index and time series data across a cluster of machines. Evaluations were conducted to examine the system’s performance using synthesized data up to 30 million data points. The evaluation results showed that, despite some drawbacks inherited from an R-tree data structure, the system can efficiently search and retrieve patterns in large time series datasets.
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Hwang, Suk Hyun. "Optimization of the Photovoltaic Time-series Analysis Process Through Hybrid Distributed Computing." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1587128073434884.
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Dias, Filipa de Carvalho. "Cluster analysis of financial time series data : evidence for portuguese and spanish stock markets." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14923.
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Mestrado em Mathematical Finance<br>Esta dissertação utilizando a distância de Caiado & Crato (2010) baseada nas autocorrelações, pretende efectuar o agrupamento de séries financeiras temporais. A métrica tenta avaliar o nível de interdependência, tendo por base a previsibilidade dos retornos. A análise de $clusters$ é feita tendo em conta a estrutura hierárquica (dendrograma) e as coordenadas principais calculadas (mapa multidimensional) das séries financeiras. Estas técnicas foram utilizadas para investigar as semelhanças e diferenças entre as empresas dos dois índices ibéricos de mercado de ações: PSI-20 e IBEX-35.<br>This paper uses the Caiado & Crato (2010) autocorrelation-based distance metric for clustering financial time series. The metric attempts to assess the level of interdependence of time series from the return predictability point of view. The cluster analysis is made by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates (multidimensional scaling map). These techniques are employed to investigate the similarities and dissimilarities between the stocks of the two Iberian stock market indexes: PSI-20 and IBEX-35.<br>info:eu-repo/semantics/publishedVersion
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Wang, Chiying. "Contributions to Collective Dynamical Clustering-Modeling of Discrete Time Series." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/198.
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The analysis of sequential data is important in business, science, and engineering, for tasks such as signal processing, user behavior mining, and commercial transactions analysis. In this dissertation, we build upon the Collective Dynamical Modeling and Clustering (CDMC) framework for discrete time series modeling, by making contributions to clustering initialization, dynamical modeling, and scaling.
We first propose a modified Dynamic Time Warping (DTW) approach for clustering initialization within CDMC. The proposed approach provides DTW metrics that penalize deviations of the warping path from the path of constant slope. This reduces over-warping, while retaining the efficiency advantages of global constraint approaches, and without relying on domain dependent constraints.
Second, we investigate the use of semi-Markov chains as dynamical models of temporal sequences in which state changes occur infrequently. Semi-Markov chains allow explicitly specifying the distribution of state visit durations. This makes them superior to traditional Markov chains, which implicitly assume an exponential state duration distribution.
Third, we consider convergence properties of the CDMC framework. We establish convergence by viewing CDMC from an Expectation Maximization (EM) perspective. We investigate the effect on the time to convergence of our efficient DTW-based initialization technique and selected dynamical models. We also explore the convergence implications of various stopping criteria.
Fourth, we consider scaling up CDMC to process big data, using Storm, an open source distributed real-time computation system that supports batch and distributed data processing.
We performed experimental evaluation on human sleep data and on user web navigation data. Our results demonstrate the superiority of the strategies introduced in this dissertation over state-of-the-art techniques in terms of modeling quality and efficiency.
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Larsson, Klara, and Freja Ling. "Time Series forecasting of the SP Global Clean Energy Index using a Multivariate LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301904.
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Clean energy and machine learning are subjects that play significant roles in shaping our future. The current climate crisis has forced the world to take action towards more sustainable solutions. Arrangements such as the UN’s Sustainable Development Goals and the Paris Agreement are causing an increased interest in renewable energy solutions. Further, the EU Taxonomy Regulation, applied in 2020, aims to scale up sustainable investments and to direct cash flows toward sustainable projects and activities. These measures create interest in investing in renewable energy alternatives and predicting future movements of stocks related to these businesses. Machine learning models have previously been used to predict time series with promising results. However, predicting time series in the form of stock price indices has, throughout previous attempts, proved to be a difficult task due to the complexity of the variables that play a role in the indices’ movements. This paper uses the machine learning algorithm long short-term memory (LSTM) to predict the S&P Global Clean Energy Index. The research question revolves around how well the LSTM model performs on this specific index and how the result is affected when past returns from correlating variables are added to the model. The researched variables are crude oil price, gold price, and interest. A model for each correlating variable was created, as well as one with all three, and one standard model which used only historical data from the index. The study found that while the model with the variable which had the strongest correlation performed best among the multivariate models, the standard model using only the target variable gave the most accurate result of any of the LSTM models.<br>Den pågående klimatkrisen har tvingat allt fler länder till att vidta åtgärder, och FN:s globala hållbarhetsmål och Parisavtalet ökar intresset för förnyelsebar energi. Vidare lanserade EU-kommissionen den 21 april 2021 ett omfattande åtgärdspaket, med syftet att öka investeringar i hållbara verksamheter. Detta skapar i sin tur ett ökat intresse för investeringar i förnyelsebar energi och metoder för att förutspå aktiepriser för dessa bolag. Maskininlärningsmodeller har tidigare använts för tidsserieanalyser med goda resultat, men att förutspå aktieindex har visat sig svårt till stor del på grund av uppgiftens komplexitet och antalet variabler som påverkar börsen. Den här uppsatsen använder sig av maskininlärningsmodellen long short-term memory (LSTM) för att förutspå S&P:s Global Clean Energy Index. Syftet är att ta reda på hur träffsäkert en LSTM-modell kan förutspå detta index, och hur resultatet påverkas då modellen används med ytterligare variabler som korrelerar med indexet. De variabler som undersöks är priset på råolja, priset på guld, och ränta. Modeller för var variabel skapades, samt en modell med samtliga variabler och en med endast historisk data från indexet. Resultatet visar att den modell med den variabel som korrelerar starkast med indexet presterade bäst bland flervariabelmodellerna, men den modell som endast användes med historisk data från indexet gav det mest träffsäkra resultatet.
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Lowry, Matthew C. "A new approach to the train algorithm for distributed garbage collection." Title page, table of contents and abstract only, 2004. http://hdl.handle.net/2440/37710.
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This thesis describes a new approach to achieving high quality distributed garbage collection using the Train Algorithm. This algorithm has been investigated for its ability to provide high quality collection in a variety of contexts, including persistent object systems and distributed object systems. Prior literature on the distributed Train Algorithm suggests that safe, complete, asynchronous, and scalable collection can be attained, however an approach that achieves this combination of behaviour has yet to emerge. The mechanisms and policies described in this thesis are unique in their ability to exploit the distributed Train Algorithm in a manner that displays all four desirable qualities. Further the mechanisms allow any number of mutator and collector threads to operate concurrently within a site; this is also a unique property amongst train-based mechanisms (distributed or otherwise). Confidence in the quality of the approach promoted in this thesis is obtained via a top-down approach. Firstly a concise behavioural model is introduced to capture fundamental requirements for safe and complete behaviour from train-based collection mechanisms. The model abstracts over the techniques previously introduced under the banner of the Train Algorithm. It serves as a self- contained template for correct train-based collection that is independent of a target object system for deployment of the algorithm. Secondly a means to instantiate the model in a distributed object system is described. The instantiation includes well-established techniques from prior literature, and via the model these are correctly refined and reorganised with new techniques to achieve asynchrony, scalability, and support for concurrency. The result is a flexible approach that allows a distributed system to exhibit a variety of local collection mechanisms and policies, while ensuring their interaction is safe, complete, asynchronous, and scalable regardless of the local choices made by each site. Additional confidence in the properties of the new approach is obtained from implementation within a distributed object system simulation. The implementation provides some insight into the practical issues that arise through the combination of distribution, concurrent execution within sites, and train-based collection. Executions of the simulation system are used to verify that safe collection is observed at all times, and obtain evidence that asynchrony, scalability, and concurrency can be observed in practice.<br>Thesis (Ph.D.)--School of Computer Science, 2004.
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Drougas, Ioannis. "Rate allocation in distributed stream processing systems." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663077971&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268240766&clientId=48051.
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Thesis (Ph. D.)--University of California, Riverside, 2008.<br>Includes abstract. Title from first page of PDF file (viewed March 10, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 93-98). Also issued in print.
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Beasley, Ben. "Assessing Blackmouth Shiner (Notropis melanostomus) habitat in the Pascagoula River using a habitat inundation index based on time series Landsat data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/honors_theses/73.
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The Blackmouth Shiner (Notropis melanostomus) is a small North American cyprinid that is listed as a Species of Concern due its relatively small range, occurring at only a few sites in Mississippi, Alabama, and Florida. Due to limited data and the small number of actual samples of N. melanostomus, the true characteristics of viable habitat and distribution remain unknown.
The objective of my research was to utilize remote sensing data to gain a better understanding of the habitat characteristics where the N. melanostomus has been collected and use this information to identify other areas were populations are likely to occur during future sampling efforts. In particular, Landsat data were used to map the spatial and temporal extent of water inundation over a 20-year time-series within floodplain water bodies surrounding the Pascagoula River to determine the effects on the presence or absence of Blackmouth Shiners at historic collection sites. These characteristics could be used to inform future site selections within the Pascagoula River drainage as well as identify other river systems that have similar inundation patterns and morphology within and proximal to the known range.
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Toups, Matthew A. "A study of three paradigms for storing geospatial data: distributed-cloud model, relational database, and indexed flat file." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2196.
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Geographic Information Systems (GIS) and related applications of geospatial data were once a small software niche; today nearly all Internet and mobile users utilize some sort of mapping or location-aware software. This widespread use reaches beyond mere consumption of geodata; projects like OpenStreetMap (OSM) represent a new source of geodata production, sometimes dubbed “Volunteered Geographic Information.” The volume of geodata produced and the user demand for geodata will surely continue to grow, so the storage and query techniques for geospatial data must evolve accordingly.
This thesis compares three paradigms for systems that manage vector data. Over the past few decades these methodologies have fallen in and out of favor. Today, some are considered new and experimental (distributed), others nearly forgotten (flat file), and others are the workhorse of present-day GIS (relational database). Each is well-suited to some use cases, and poorly-suited to others. This thesis investigates exemplars of each paradigm.
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Evkaya, Ozan Omer. "Modelling Weather Index Based Drought Insurance For Provinces In The Central Anatolia Region." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614572/index.pdf.
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Drought, which is an important result of the climate change, is one of the most serious natural hazards globally. It has been agreed all over the world that it has adverse impacts on the production of agriculture, which plays a major role in the economy of a country. Studies showed that the results of the drought directly affected the crop yields, and it seems that this negative impact will continue drastically soon. Moreover, many researches revealed that, Turkey will be affected from the results of climate change in many aspects, especially the agricultural production will encounter dry seasons after the rapid changes in the precipitation amount. Insurance
is a well-established method, which is used to share the risk based on natural disasters by people and organizations. Furthermore, a new way of insuring against the weather shocks is designing index-based insurance, and it has gained special attention in many developing countries. In this study, our aim is to model weather index based drought insurance product to help the small holder farmers in the Cental Anatolia Region under different models. At first, time series techniques were applied to forecast the wheat yield relying on the past data. Then, the AMS (AgroMetShell) software outputs, NDVI (Normalized Difference Vegetation Index) values were used, and SPI values for distinct time steps were chosen to develop a basic threshold based drought insurance for each province. Linear regression equations were used to calculate the trigger points for weather index, afterwards based on these trigger levels<br>pure premium and indemnity calculations were made for each province separately. In addition to this, Panel Data Analysis were used to construct an alternative linear model for drought insurance. It can be helpful to understand the direct and actual effects of selected weather index measures on wheat yield and also reduce the basis risks for constructed contracts. A simple ratio was generated to compare the basis risk of the different index-based insurance contracts.
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Su, Yu. "Big Data Management Framework based on Virtualization and Bitmap Data Summarization." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420738636.
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Minas, Michael Getachew. "Characterization of plant-water interaction in Kilombero River Catchment in Tanzania using Normalized Difference Vegetation Index (NDVI)." Thesis, Stockholms universitet, Institutionen för naturgeografi och kvartärgeologi (INK), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-110913.
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Remote-sensing based indices such as Normalized Difference Vegetation Index have yielded valuable information about plant health. As the availability of water is one of the factors that controls plant's response to their environment, it is possible to indirectly studythe hydrology of an area via vegetation indices. Hence the thesis work used this tool to characterize the potential shifts in vegetation cover within and between years in Kilombero river catchment in Tanzania and make connection to the hydrology in the area. Separate time series analyses conducted on data pertaining to NDVI values and the areal coverage variability of arbitrarily defined NDVI-classes. The former data was extracted from a naturally vegetated wetland in the middle of the catchment while the latter from the topographically defined areas of the catchment. Results from the analyses showed that bothdatasets are sensitive to the seasonal rainfall while at inter-annual scale the areal coverage variability displayed significant correlations with past precipitation. Meanwhile the relatively higher sensitivity of the lowland area‟s NDVI to precipitation conforms to the initial assumption which emphasizes the importance of the wetland sub-catchment codenamed 1KB17 in describing Kilombero‟s hydrology. But the datasets show weak trends and it was not possible to make accurate future predictions on the hydrological conditions in the area. Meteorological distortions like clouds and environmental processes such as climate patterns or disturbances might have caused the problem in trend detection. Further studies needed to shed more light on the connection between land cover and hydrologic response in Kilombero.
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Ishii, Renato Porfirio. "Otimização de operações de entrada e saída visando reduzir o tempo de resposta de aplicações distribuídas que manipulam grandes volumes de dados." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-23092010-170110/.
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Aplicações científicas atuais têm produzido volumes de dados cada vez maiores. O processamento, a manipulação e a análise desses dados requerem infraestruturas computacionais de larga escala tais como aglomerados e grades de computadores. Nesse contexto, várias pesquisas visam o aumento de desempenho dessas aplicações por meio da otimização de acesso a dados. Para alcançar tal objetivo, pesquisadores têm utilizado técnicas de replicação, migração, distribuição e paralelismo de dados. No entanto, uma das principais lacunas dessas pesquisas está na falta de emprego de conhecimento sobre aplicações com objetivo de realizar essa otimização. Essa lacuna motivou esta tese que visa empregar comportamento histórico e preditivo de aplicações a fim de otimizar suas operações de leitura e escrita sobre dados distribuídos. Os estudos foram iniciados empregando-se informações previamente monitoradas de aplicações a fim de tomar decisões relativas à replicação, migração e manutenção de consistência. Observou-se, por meio de uma nova heurística, que um conjunto histórico de eventos auxilia a estimar o comportamento futuro de uma aplicação e otimizar seus acessos. Essa primeira abordagem requer ao menos uma execução prévia da aplicação para composição de histórico. Esse requisito pode limitar aplicações reais que apresentam mudanças comportamentais ou que necessitam de longos períodos de execução para completar seu processamento. Para superar essa limitação, uma segunda abordagem foi proposta baseada na predição on-line de eventos comportamentais de aplicações. Essa abordagem não requer a execução prévia da aplicação e permite adaptar estimativas de comportamento futuro em função de alterações adjacentes. A abordagem preditiva analisa propriedades de séries temporais com objetivo de classificar seus processos geradores. Essa classificação aponta modelos que melhor se ajustam ao comportamento das aplicações e que, portanto, permitem predições com maior acurácia. As duas abordagens propostas foram implementadas e avaliadas utilizando o simulador OptorSim, vinculado ao projeto LHC/CERN, amplamente adotado pela comunidade científica. Experimentos constataram que as duas abordagens propostas reduzem o tempo de resposta (ou execução) de aplicações que manipulam grandes volumes de dados distribuídos em aproximadamente 50%<br>Current scientific applications produce large amount of data and handling, processing and analyzing such data require large-scale computing infrastructure such as clusters and grids. In this context, various studies have focused at improving the performance of these applications by optimizing data access. In order to achieve this goal, researchers have employed techniques of replication, migration, distribution and parallelism of data. However, these common approaches do not use knowledge about the applications at hand to perform this optimization. This gap motivated the present thesis, which aims at applying historical and predictive behavior of applications to optimize their reading and writing operations on distributed data. Based on information previously monitored from applications to make decisions regarding replication, migration and consistency of data, a new heuristic was initially proposed. Its evaluation revealed that considering sets of historical events indeed helps to estimate the behavior of future applications and to optimize their access operations. Thus it was embedded into two optimization approaches. The first one requires at least a previous execution for the history composition. This requirement may limit real world applications which present behavioral changes or take very long time to execute. In order to overcome this issue, a second technique was proposed. It performs on-line predictions about the behavior of the applications, mitigating the need of any prior execution. Additionally, this approach considers the future behavior of an application as a function of its underlying changes. This behavior can be modeled as time series. The method works by analyzing the series properties in order to classify their generating processes. This classification indicates models that best fit the applications behavior, allowing more accurate predictions. Experiments using the OptorSim simulator (LHC/CERN project) confirmed that the proposed approaches are able to reduce the response time of applications that handle large amount of distributed data in approximately 50%
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Osunmadewa, Babatunde A., Christine Wessollek, and Pierre Karrasch. "Linear and segmented linear trend detection for vegetation cover using GIMMS normalized difference vegetation index data in semiarid regions of Nigeria." SPIE, 2015. https://tud.qucosa.de/id/qucosa%3A35266.
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Quantitative analysis of trends in vegetation cover, especially in Kogi state, Nigeria, where agriculture plays a major role in the region’s economy, is very important for detecting long-term changes in the phenological behavior of vegetation over time. This study employs the use of normalized difference vegetation index (NDVI) [global inventory modeling and mapping studies 3g (GIMMS)] data from 1983 to 2011 with detailed methodological and statistical approach for analyzing trends within the NDVI time series for four selected locations in Kogi state. Based on the results of a comprehensive study of seasonalities in the time series, the original signals are decomposed. Different linear regression models are applied and compared. In order to detect structural changes over time a detailed breakpoint analysis is performed. The quality of linear modeling is evaluated by means of statistical analyses of the residuals. Standard deviations of the regressions are between 0.015 and 0.021 with R2 of 0.22–0.64. Segmented linear regression modeling is performed for improvement and a decreasing standard deviation of 33%–40% (0.01–0.013) and R2 up to 0.82 are obtained. The approach used in this study demonstrates the added value of long-term time series analyses of vegetation cover for the assessment of agricultural and rural development in the Guinea savannah region of Kogi state, Nigeria.
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Khaled, Fawaz. "Credit default and the real estate market." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/14478.
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Evidence from various countries over the past two decades proves that swings in house prices have been concomitant with financial instability. The history of financial crises shows that the six biggest banking crises in advanced economies were accompanied by housing busts. Despite the abundance of literature on the forces behind the financial crisis, and in particular studies investigating the connections between financial stability and disturbances in the real estate market, fundamental questions still wait for convincing answers, such as: (i) To what extent is regional heterogeneity in property price increases reflected in dissimilarity in the evolution of credit default? (ii) What role do borrower-related factors such as housing affordability and household indebtedness, and financial market-related factors such as financial developments, play on the growth of bad loans as a main concern for banking sector? (iii) To which extent do banks’ lending behaviour and property prices undermine the stability of the banking sector, and what are the directions of causality between credit defaults, property prices and banks’ lending behaviour? The goal of this thesis is to investigate these issues and explain the practical implications of the findings. This thesis contains three empirical essays. The first essay explores the nexus between house prices and non-performing loans (NPLs), concentrating on the extent to which geographical variations in house prices are translated into regional variations in credit defaults. The stochastic dominance approach has been used for this purpose, with 372 individual US banks. The stochastic dominance analyses disclose symmetric behaviour between NPLs and the scale of house price increments. The essay is further extended by employing Arellano and Bond’s (1991) GMM model to explore the effect of GDP, unemployment rates, lending interest rates and house prices on the growth of NPLs. The outcomes of the GMM estimations reveal a high explanatory power of economic growth, unemployment and lending interest rates on NPLs. In an additional analysis, a generalised panel threshold model is estimated to check for the presence of a threshold point, above which different impacts of house prices might be found. The threshold model specifications provide a threshold point, in relation to which two different impacts of house prices on the evolution of NPLs are estimated. A general consensus in the literature attributes credit defaults to a wide-ranging spectrum of drivers that take into consideration borrower-related factor, lender-related factors and factors related to financial and real estate markets. The second essay attempts to answer the second question mentioned above, by investigating the impact of borrower-related factors, lender-related factors and financial market-related factors in driving NPLs. The impact of these factors on the evolution of impaired loans is explored by estimating fixed effect models then the analysis is extended to dynamic models using the GMM procedure on an annual balanced panel dataset. Household vulnerability, financial developments and housing affordability are found to be significant contributors to the growth of NPLs. The interaction mechanism between the real estate market and the financial system has often been blamed for being the root of financial crises, through the accumulation of housing market bubbles that leads to the ultimate collapse of the financial markets. The third essay, using the Autoregressive Distributed Lag technique, looks for the presence of cointegrating relationships between mortgage defaults, property prices and bank lending in Hong Kong. Our findings reveal evidence of cointegrating relationships between bank lending, property prices and mortgage defaults in the long term, which governs the correction mechanism between these variables. These outcomes call for more effort to be devoted to maintaining a balanced relationship between these factors. The essay also finds evidence of short-term dynamics between these variables. Importantly, loan-to-value is found to play the most effective role in curbing mortgage default risk in the portfolios of the Hong Kong banking sector.
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Langston, Jeremy W. "Availability and performance analysis of data hot-spots in distributed storage systems a thesis presented to the faculty of the Graduate School, Tennessee Technological University /." Click to access online, 2009. http://proquest.umi.com/pqdweb?index=0&did=1797609571&SrchMode=1&sid=2&Fmt=6&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268408220&clientId=28564.
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Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.
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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM.
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Tajudeen, Ibrahim. "Essays on energy efficiency and fuel subsidy reforms." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/essays-on-energy-efficiency-and-fuel-subsidy-reforms(3066138a-809f-4a4f-aeaf-a1e5f6087891).html.
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This thesis uses innovative approaches to analyse energy policy interventions aimed at enhancing the environmental sustainability of energy use as well as its consequential welfare implications. First, we examine the relationship between energy efficiency improvement and CO2 emissions at the macro level. We use the Index Decomposition Analysis to derive energy efficiency by separating out the impact of shifts in economic activity on energy intensity. We then employ econometric models to relate energy efficiency and CO2 emissions accounting for non-economic factors such as consumers lifestyle and attitudes. The applications for 13 OPEC and 30 OECD countries show that at the country-group and individual country level, increase in energy intensity for OPEC is associated with both deteriorations in energy efficiency and shifts towards energy-intensive activities. The model results suggest that the reduction in energy efficiency in general go in tandem with substantial increases in CO2 emissions. The decline in energy intensity for OECD can be attributed mainly to improvements in energy efficiency which is found to compensate for the impact on CO2 emissions of income changes. The results confirm the empirical relevance of energy efficiency improvements for the mitigation of CO2 emissions. The method developed in this chapter further enables the separate assessment of non-economic behavioural factors which according to the results exert a non-trivial influence on CO2 emissions. Secondly, having empirically confirmed the relationship between energy efficiency improvements and CO2 emission at the macro level in Chapter 2, we investigate potential underlying drivers of energy efficiency improvements taking into account potential asymmetric effects of energy price change in Chapter 3. This is crucial for designing effective and efficient policy measures that can promote energy efficiency. In addition to the Index Decomposition Analysis used to estimate the economy-wide energy efficiency in Chapter 2, we also use Stochastic Frontier Analysis and Data Envelop Analysis as alternative methods. The driving factors are examined using static and dynamic panel model methods that account for both observed and unobserved country heterogeneity. The application for 32 OECD countries shows that none of the three methods leads to correspondence in term of ranking between energy efficiency estimates and energy intensity at the country level corroborating the criticism that energy intensity is a poor proxy for energy efficiency. The panel-data regression results using the results of the three methods show similarities in the impacts of the determinants on the energy efficiency levels. Also, we find insignificant evidence of asymmetric effects of total energy price but there is proof of asymmetry using energy specific prices. Thirdly, in Chapter 4 we offer an improved understanding of the impacts to expect of abolishing fuel price subsidy on fuel consumption, and also of the welfare and distributional impacts at the household level. We develop a two-step approach for this purpose. Key aspect of the first step is a two-stage budgeting model to estimate various fuel types elasticities using micro-data. Relying on these estimates and the information on households expenditure shares for different commodities, the second step estimates the welfare (direct and indirect) and distributional impacts. The application for Nigeria emphasises the relevance of this approach. We find heterogeneous elasticities of fuel demand among household groups. The distributional impact of abolishing the kerosene subsidy shows a regressive welfare loss. Although we find a progressive loss for petrol, the loss gap between the low- and high-income groups is small relative to the loss gap from stopping kerosene subsidy, making the low-income groups to suffer a higher total welfare loss. Finally, from the highlighted results, we draw the following concluding remarks in chapter 5. Energy efficiency appears a key option to mitigate CO2 emissions but there is also a need for additional policies aiming for behavioural change; energy specific prices and allowing for asymmetry in analysing the changes in energy efficiency is more appropriate and informative in formulating reliable energy policies; the hypothesis that only the rich would be worse-off from fuel subsidy removal is rejected and the results further suggest that timing of the fuel subsidy removal would be crucial as a higher international oil price will lead to higher deregulated fuel price and consequently, larger welfare loss.
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Huang, Yu-che, and 黃郁哲. "Hierarchical Distributed Index and File System for Astronomical Observation Data." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/78289777950100998346.
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碩士<br>國立中央大學<br>資訊工程學系<br>102<br>The data of star observation is huge and increase continuously. Therefore it needs strong hardware to store these data. If these data lacks of a efficiently manage methodology, users will need to find and get data themselves, it’s not a good and efficient way. In order to solve this problem, this paper proposes a hierarchical design distributed file system to manage these astronomical observation data. First, this system uses Hierarchical Triangular Mesh to compute a key for every star, the values of these keys are sequential, so this paper links the servers in the system to form a ring structure, and let every server manages an unrepeated range of keys, range that every two adjacent servers manage are also continuous. Besides, The reason why adopting ring structure is due to its scalability, fault tolerance and availability, these advantages build the good foundation of the system.
For increase the efficiency of query, the system needs execute multiple services. To reduce the load of servers, this paper builds three level of the ring structure and allocates these services to different layer. These layers are 1. Query Transaction, 2. Data index, 3. Storage. This design not only reduces the load, but also makes these services independent.
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Cheng, Kai-Hao, and 鄭凱豪. "Distributed Large-Scale Astronomical Data Management System Based on HTM Index." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/12886574346450783248.
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碩士<br>國立中央大學<br>資訊工程研究所<br>100<br>Astronomical researchers generally store the observation data into the connectionless servers. The number of the data grows through the time pass by. When the users want to query the data, they should manually query from each server. It’s not only really inconvenienced but also cost much time and work.
For solving this problem, we intend to design a distributed Chord-like P2P system and combine the HTM spatial index to the system. Astronomical data distributed to the computers on the ring system and contributed the spatial index with their own data. The computers will ping the neighbors in the ring system each period of the time to detect the disconnecting peers. System will also automatically detect each peer’s loading. When the loading difference exceeds the threshold, system would perform load balancing. User can query the data from any computer in the distributed system. System will pass the query message to the computers that meet the conditions and each computer then search the data efficiently through the HTM index.
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Duarte, Eduardo Miguel Oliveira. "Collaborative analysis over massive time series data sets." Master's thesis, 2018. http://hdl.handle.net/10773/26170.
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The recent expansion of metrification on a daily basis has led to the production
of massive quantities of data, and in many cases, these collected metrics
are only useful for knowledge building when seen as a full sequence of
data ordered by time, which constitutes a time series. To find and interpret
meaningful behavioral patterns in time series, a multitude of analysis software
tools have been developed. Many of the existing solutions use annotations
to enable the curation of a knowledge base that is shared between a group
of researchers over a network. However, these tools also lack appropriate
mechanisms to handle a high number of concurrent requests and to properly
store massive data sets and ontologies, as well as suitable representations
for annotated data that are visually interpretable by humans and explorable by
automated systems. The goal of the work presented in this dissertation is to
iterate on existing time series analysis software and build a platform for the
collaborative analysis of massive time series data sets, leveraging state-of-the-art technologies for querying, storing and displaying time series and annotations.
A theoretical and domain-agnostic model was proposed to enable
the implementation of a distributed, extensible, secure and high-performant
architecture that handles various annotation proposals in simultaneous and
avoids any data loss from overlapping contributions or unsanctioned changes.
Analysts can share annotation projects with peers, restricting a set of collaborators
to a smaller scope of analysis and to a limited catalog of annotation
semantics. Annotations can express meaning not only over a segment of time,
but also over a subset of the series that coexist in the same segment. A novel
visual encoding for annotations is proposed, where annotations are rendered
as arcs traced only over the affected series’ curves in order to reduce visual
clutter. Moreover, the implementation of a full-stack prototype with a reactive
web interface was described, directly following the proposed architectural and
visualization model while applied to the HVAC domain. The performance of
the prototype under different architectural approaches was benchmarked, and
the interface was tested in its usability. Overall, the work described in this dissertation
contributes with a more versatile, intuitive and scalable time series
annotation platform that streamlines the knowledge-discovery workflow.<br>A recente expansão de metrificação diária levou à produção de quantidades
massivas de dados, e em muitos casos, estas métricas são úteis para
a construção de conhecimento apenas quando vistas como uma sequência
de dados ordenada por tempo, o que constitui uma série temporal. Para se
encontrar padrões comportamentais significativos em séries temporais, uma
grande variedade de software de análise foi desenvolvida. Muitas das soluções
existentes utilizam anotações para permitir a curadoria de uma base
de conhecimento que é compartilhada entre investigadores em rede. No entanto,
estas ferramentas carecem de mecanismos apropriados para lidar com
um elevado número de pedidos concorrentes e para armazenar conjuntos
massivos de dados e ontologias, assim como também representações apropriadas
para dados anotados que são visualmente interpretáveis por seres
humanos e exploráveis por sistemas automatizados. O objetivo do trabalho
apresentado nesta dissertação é iterar sobre o software de análise de séries
temporais existente e construir uma plataforma para a análise colaborativa
de grandes conjuntos de séries temporais, utilizando tecnologias estado-de-arte
para pesquisar, armazenar e exibir séries temporais e anotações. Um
modelo teórico e agnóstico quanto ao domínio foi proposto para permitir a
implementação de uma arquitetura distribuída, extensível, segura e de alto
desempenho que lida com várias propostas de anotação em simultâneo e
evita quaisquer perdas de dados provenientes de contribuições sobrepostas
ou alterações não-sancionadas. Os analistas podem compartilhar projetos
de anotação com colegas, restringindo um conjunto de colaboradores a uma
janela de análise mais pequena e a um catálogo limitado de semântica de
anotação. As anotações podem exprimir significado não apenas sobre um
intervalo de tempo, mas também sobre um subconjunto das séries que coexistem
no mesmo intervalo. Uma nova codificação visual para anotações é
proposta, onde as anotações são desenhadas como arcos traçados apenas
sobre as curvas de séries afetadas de modo a reduzir o ruído visual. Para
além disso, a implementação de um protótipo full-stack com uma interface
reativa web foi descrita, seguindo diretamente o modelo de arquitetura e visualização
proposto enquanto aplicado ao domínio AVAC. O desempenho do
protótipo com diferentes decisões arquiteturais foi avaliado, e a interface foi
testada quanto à sua usabilidade. Em geral, o trabalho descrito nesta dissertação
contribui com uma abordagem mais versátil, intuitiva e escalável para
uma plataforma de anotação sobre séries temporais que simplifica o fluxo de
trabalho para a descoberta de conhecimento.<br>Mestrado em Engenharia Informática
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Rodrigues, Arlete da Silva. "Analysis of vegetation dynamics using time-series vegetation index data from Earth Observation Satellites." Doctoral thesis, 2014. https://repositorio-aberto.up.pt/handle/10216/77510.
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Rodrigues, Arlete da Silva. "Analysis of vegetation dynamics using time-series vegetation index data from Earth Observation Satellites." Tese, 2014. https://repositorio-aberto.up.pt/handle/10216/77510.
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Lin, Chi-Hung, and 林其鴻. "Data Mining in the Application of Financial Time Series Prediction-Empirical Results from Nikkei 225 Stock Index and Index Futures." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/45118024570485781073.
Full textAbstract:
碩士<br>輔仁大學<br>金融研究所<br>93<br>This study applies back-propagation neural network and support vector machines to predict the opening price of the Nikkei 225 index. The purpose of this thesis is to help investors use effective and accessible variables to forecast the opening price of the market and compare the forecasting accuracy with different data frequency, then see whether the high frequency data could provide more extra market information. Disregarding the complicated fundamental and technical indicators, this study simply considers the Nikkei 225 index futures price between the previous cash market closing time and the subsequent daily opening time and several important American stock index futures price as the inputs of our opening price prediction models.
In order to verify the feasibility and effectiveness of our proposed approach, the daily trading data from October 4st, 1999 to September 30, 2004 is used in this study. The empirical results show that the back-propagation neural networks opening price prediction model using the previous CME’s Nikkei 225 index futures closing price, previous CME’s Japanese Yen futures closing price and the previous OSE’s Nikkei 225 index futures closing price as the inputs provides best forecasting result while the support vector regression opening price prediction model using the CME’s Nikkei 225 index futures closing price and previous OSE’s Nikkei 225 index futures closing price as the inputs has best forecasting capability. Besides, high frequency data can provide more market information in estimating the opening price of Nikkei 225 for the back-propagation neural network, but the result doesn’t hold for the support vector regression model.
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Jehangiri, Ali Imran. "Distributed Anomaly Detection and Prevention for Virtual Platforms." Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0022-605F-2.
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"System Support for Large-scale Geospatial Data Analytics." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.62651.
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abstract: The volume of available spatial data has increased tremendously. Such data includes but is not limited to: weather maps, socioeconomic data, vegetation indices, geotagged social media, and more. These applications need a powerful data management platform to support scalable and interactive analytics on big spatial data. Even though existing single-node spatial database systems (DBMSs) provide support for spatial data, they suffer from performance issues when dealing with big spatial data. Challenges to building large-scale spatial data systems are as follows: (1) System Scalability: The massive-scale of available spatial data hinders making sense of it using traditional spatial database management systems. Moreover, large-scale spatial data, besides its tremendous storage footprint, may be extremely difficult to manage and maintain due to the heterogeneous shapes, skewed data distribution and complex spatial relationship. (2) Fast analytics: When the user runs spatial data analytics applications using graphical analytics tools, she does not tolerate delays introduced by the underlying spatial database system. Instead, the user needs to see useful information quickly.
In this dissertation, I focus on designing efficient data systems and data indexing mechanisms to bolster scalable and interactive analytics on large-scale geospatial data. I first propose a cluster computing system GeoSpark which extends the core engine of Apache Spark and Spark SQL to support spatial data types, indexes, and geometrical operations at scale. In order to reduce the indexing overhead, I propose Hippo, a fast, yet scalable, sparse database indexing approach. In contrast to existing tree index structures, Hippo stores disk page ranges (each works as a pointer of one or many pages) instead of tuple pointers in the indexed table to reduce the storage space occupied by the index. Moreover, I present Tabula, a middleware framework that sits between a SQL data system and a spatial visualization dashboard to make the user experience with the dashboard more seamless and interactive. Tabula adopts a materialized sampling cube approach, which pre-materializes samples, not for the entire table as in the SampleFirst approach, but for the results of potentially unforeseen queries (represented by an OLAP cube cell).<br>Dissertation/Thesis<br>Doctoral Dissertation Computer Science 2020
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Lowry, Matthew C. "A new approach to the train algorithm for distributed garbage collection." Thesis, 2004. http://hdl.handle.net/2440/37710.
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This thesis describes a new approach to achieving high quality distributed garbage collection using the Train Algorithm. This algorithm has been investigated for its ability to provide high quality collection in a variety of contexts, including persistent object systems and
distributed object systems. Prior literature on the distributed Train Algorithm suggests that safe, complete, asynchronous, and scalable collection can be attained, however an approach that achieves this combination of behaviour has yet to emerge. The mechanisms and policies described in this thesis are unique in their ability to exploit the distributed Train Algorithm in a manner that displays all four desirable qualities. Further the mechanisms allow any number of mutator and collector threads to operate concurrently within a site; this is also a unique property amongst train-based mechanisms (distributed or otherwise). Confidence in the quality of the approach promoted in this thesis is obtained via a top-down approach. Firstly a concise behavioural model is introduced to capture fundamental requirements for safe and complete behaviour from train-based collection mechanisms. The model abstracts over the techniques previously introduced under the banner of the Train Algorithm. It serves as a self- contained template for correct train-based collection that is independent of a target object system for deployment of the algorithm. Secondly a means to instantiate the model in a distributed object system is described. The instantiation includes well-established techniques from prior literature, and via the model these are correctly refined and reorganised with new techniques to achieve asynchrony, scalability, and support for concurrency. The result is a flexible approach that allows a distributed system to exhibit a variety of local collection mechanisms and policies, while ensuring their interaction is safe, complete, asynchronous, and scalable regardless of the local choices made by each site.
Additional confidence in the properties of the new approach is obtained from implementation within a distributed object system simulation. The implementation provides some insight into the practical issues that arise through the combination of distribution, concurrent execution within sites, and train-based collection. Executions of the simulation system are used to verify that safe collection is observed at all times, and obtain evidence that asynchrony, scalability, and concurrency can be observed in practice.<br>Thesis (Ph.D.)--University of Adelaide, School of Computer Science, 2004.
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Ogbokor, Cyril Ayetuoma. "Foreign trade and economic growth in Namibia : a time series analysis / Cyril Ayetuoma Ogbokor." Thesis, 2015. http://hdl.handle.net/10394/16546.
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Foreign trade is increasingly becoming a powerful tool when it comes to the promotion of economic growth in modern economies. This is especially so in the face of the continued rise of globalisation. In consideration of this fact, this thesis assessed the impact of foreign trade on the growth process of Namibia’s economy for the period stretching from 1990 to 2012. This main objective was further developed into primary, theoretical and empirical objectives. In order to realise these multiple objectives, two modern econometric time series techniques were employed, namely vector autoregressive (VAR) and auto-regression distributed lag (ARDL) models. Based on these two techniques, the following procedures featured during the study: Stationary tests, error correction modelling, co-integration tests, Granger causality tests, generalised impulse response functions and generalised forecast error variance decomposition. The following constitutes the main findings arising from this study: First, the study found that there is a positive relationship among the variables that were investigated. Indeed, this positive relationship suggests that the economy of Namibia can be expanded potentially by means of foreign trade. The result is also in line with economic theory. Secondly, the empirical findings also show that export, foreign direct investment and exchange rate endogenously respond to shocks in economic growth. Thirdly, economic growth itself accounted for most of the innovations that occurred during the period under consideration concerning economic growth. Fourthly, amongst the three explanatory variables used in the model, exports and foreign direct investment contributed more towards innovations in economic growth during the forecast period. Initially, exports and foreign direct investment dominated over the forecast horizon with each contributing almost an equal share of over 5 percent after 12 quarters.
Thereafter, exports’ contribution relatively exceeded that of foreign direct investment. Fifthly, it is particularly important to note that the exchange rate variable made the weakest contribution towards explaining economic growth for the forecast period of 24 quarters.
In consideration of the general constraints associated with this study, the thesis puts forward a number of proposals for possible further investigation by any theorist who is keen about probing the issue that the thesis investigated. The thesis considers the following as its significant contributions to the existing literature: First, this study primarily examined the relationship between exports and economic growth. By adding the effect of foreign direct investment and exchange rate to the analysis, this study became more comprehensive. This further widens the scope for policymaking for Namibia, as well as other developing economies on a similar route. Secondly, the study employed two modern econometric time series techniques, namely VAR and ARDL models in investigating the research topic under consideration. Most of the related studies that were reviewed either utilised ordinary least squares (OLS) or VAR or ARDL approach on its own. By implication, the results obtained from this study, therefore, are from a technical point of view more robust. Thirdly, through constructive comments, this thesis made valuable contributions to the relevant empirical literature as reviewed during the course of the study. Fourthly, since this study has a focus on Namibia, it provided the opportunity for the thesis to present a comprehensive analysis on issues pertaining to Namibia specifically. Lastly, the various recommendations put forward by this thesis will assist Namibia, as well as other developing countries, on a related path when it comes to formulating policies for the promotion of exports in particular and economic growth in general.<br>PhD (Economics)--North-West University, Vaal Triangle Campus, 2015.
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Shen, Wei-Li, and 沈偉立. "Prediction of Time-Series Data by Integration of Regression Trees and Artificial Neural NetworkFor Example, Taiwan Stock Index Future and Gross National Product." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/r4r82a.
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碩士<br>大同大學<br>資訊經營學系(所)<br>95<br>The related researches showed Artificial Neural Network technology have higher forecast accuracy of Prediction, the premise is use suitable parameters to network model, Because of the appropriate parameters design can have better prediction accuracy compared to the statistical method thought adopting more than data indication for analysis is more easily to find the relevant parameter. On contrariety, it will involve the unnecessarily interference and conduction in the prediction accuracy reduction.
This research method combines Regression Trees with Artificial Neural Network technology to Taiwan Stock Index Future prediction, using the Liner Regression and Regression Trees as filters to find the indexes that affect the FIFX. Then adapting to Neural Network Model as input. The experiential results shows the MSE of our method is only 0.13 which is better than the previous research 0.15.
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Chaudhry, Mohammad. "Network Coding in Distributed, Dynamic, and Wireless Environments: Algorithms and Applications." Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10529.
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The network coding is a new paradigm that has been shown to improve throughput, fault tolerance, and other quality of service parameters in communication networks. The basic idea of the network coding techniques is to relish the "mixing" nature of the information flows, i.e., many algebraic operations (e.g., addition, subtraction etc.) can be performed over the data packets. Whereas traditionally information flows are treated as physical commodities (e.g., cars) over which algebraic operations can not be performed. In this dissertation we answer some of the important open questions related to the network coding. Our work can be divided into four major parts.
Firstly, we focus on network code design for the dynamic networks, i.e., the networks with frequently changing topologies and frequently changing sets of users. Examples of such dynamic networks are content distribution networks, peer-to-peer networks, and mobile wireless networks. A change in the network might result in infeasibility of the previously assigned feasible network code, i.e., all the users might not be able to receive their demands. The central problem in the design of a feasible network code is to assign local encoding coefficients for each pair of links in a way that allows every user to decode the required packets. We analyze the problem of maintaining the feasibility of a network code, and provide bounds on the number of modifications required under dynamic settings. We also present distributed algorithms for the network code design, and propose a new path-based assignment of encoding coefficients to construct a feasible network code.
Secondly, we investigate the network coding problems in wireless networks. It has been shown that network coding techniques can significantly increase the overall
throughput of wireless networks by taking advantage of their broadcast nature. In wireless networks each packet transmitted by a device is broadcasted within a certain
area and can be overheard by the neighboring devices. When a device needs to transmit packets, it employs the Index Coding that uses the knowledge of what the device's neighbors have heard in order to reduce the number of transmissions. With the Index Coding, each transmitted packet can be a linear combination of the original packets. The Index Coding problem has been proven to be NP-hard, and NP-hard to approximate. We propose an efficient exact, and several heuristic solutions for the Index Coding problem. Noting that the Index Coding problem is NP-hard to approximate, we look at it from a novel perspective and define the Complementary Index Coding problem, where the objective is to maximize the number of transmissions that are saved by employing coding compared to the solution that does not involve coding. We prove that the Complementary Index Coding problem can be approximated in several cases of practical importance. We investigate both the multiple unicast and multiple multicast scenarios for the Complementary Index Coding problem for computational complexity, and provide polynomial time approximation algorithms.
Thirdly, we consider the problem of accessing large data files stored at multiple locations across a content distribution, peer-to-peer, or massive storage network. Parts of the data can be stored in either original form, or encoded form at multiple network locations. Clients access the parts of the data through simultaneous downloads from several servers across the network. For each link used client has to pay some cost. A client might not be able to access a subset of servers simultaneously due to network restrictions e.g., congestion etc. Furthermore, a subset of the servers might contain correlated data, and accessing such a subset might not increase amount of information at the client. We present a novel efficient polynomial-time solution for this problem that leverages the matroid theory.
Fourthly, we explore applications of the network coding for congestion mitigation and over flow avoidance in the global routing stage of Very Large Scale Integration
(VLSI) physical design. Smaller and smarter devices have resulted in a significant increase in the density of on-chip components, which has given rise to congestion
and over flow as critical issues in on-chip networks. We present novel techniques and algorithms for reducing congestion and minimizing over flows.
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Holtby, Dan. "Lower bound for scalable Byzantine agreement." Thesis, 2006. http://hdl.handle.net/1828/2069.
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We consider the problem of computing Byzantine Agreement in a synchronous network with n processors each with a private random string, where each pair of processors is connected by a private communication line. The adversary is malicious and non-adaptive, i.e., it must choose the processors to corrupt at the start of the algorithm. Byzantine Agreement is known to be computable in this model in an expected constant number of rounds. We consider a scalable model where in each round each uncorrupt processor can send to any set of log n other processors and listen to any set of log n processors. We define the loss of a computation to be the number of uncorrupt processors whose output, does not agree with the output of the majority of uncorrupt processors, We show that. if there are I corrupt processors, then any randomised protocol which has probability at least 1/2 -h 1/ log u of loss less
than t 2/3 / 16fn1/3log5/3n requires at least f rounds.
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Sun, Le. "Data stream mining in medical sensor-cloud." Thesis, 2016. https://vuir.vu.edu.au/31032/.
Full textAbstract:
Data stream mining has been studied in diverse application domains. In recent
years, a population aging is stressing the national and international health care
systems. Along with the advent of hundreds and thousands of health monitoring
sensors, the traditional wireless sensor networks and anomaly detection
techniques cannot handle huge amounts of information. Sensor-cloud makes
the processing and storage of big sensor data much easier. Sensor-cloud is an
extension of Cloud by connecting the Wireless Sensor Networks (WSNs) and
the cloud through sensor and cloud gateways, which consistently collect and
process a large amount of data from various sensors located in different areas.
In this thesis, I will focus on analysing a large volume of medical sensor data
streams collected from Sensor-cloud. To analyse the Medical data streams, I
propose a medical data stream mining framework, which is targeted on tackling
four main challenges ...
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潮木, 守一, 城司 菊池, 真和 矢野 та 英典 藤田. "教育システムの動態分析のための指標開発とデータベース作成". 1987. http://hdl.handle.net/2237/12853.
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