Submitted by Élida Maeli Fernandes Quirino (maely_sax@hotmail.com) on 2018-05-15T13:05:55Z No. of bitstreams: 1 FRNACISCO ARICLENES OLINTO - DISSERTAÇÃO PPGSA PROFISSIONAL 2014..pdf: 1640106 bytes, checksum: b19b02a5c2c2ea22def9e997924518db (MD5) / Made available in DSpace on 2018-05-15T13:05:55Z (GMT). No. of bitstreams: 1 FRNACISCO ARICLENES OLINTO - DISSERTAÇÃO PPGSA PROFISSIONAL 2014..pdf: 1640106 bytes, checksum: b19b02a5c2c2ea22def9e997924518db (MD5) Previous issue date: 2014-12-16 / A apicultura é uma das poucas atividades agropecuárias que atende aos três requisitos da sustentabilidade: o econômico, o social e o ecológico. Sendo assim, fornece renda para o apicultor, ocupa mão de obra familiar ou contratada e contribui para a preservação da flora nativa. A abelha é importante para a economia mundial como polinizadora, aumentando a produção de frutos e sementes, e como fornecedora de mel, cera, geleia real, própolis, pólen e veneno (apitoxina). Esses produtos são muito procurados para a matéria-prima das indústrias farmacêutica, alimentícia e cosmética e para consumo em forma natural. A sanidade pode afetar o desenvolvimento da apicultura, pois a Apis mellifera como qualquer outro organismo vivo, é susceptível a doenças causadas por bactérias, vírus, fungos e outros parasitas e as desordens metabólicas, nutricionais e hormonais, além de intoxicações diversas. Assim, em virtude da necessidade de estudos a respeito da sanidade apícola, objetiva-se estudar o comportamento higiênico e a identificação da varroatose e nosemose em colônias de abelhas Apis mellifera em apiários localizados no Sertão do Estado da Paraíba. A pesquisa foi realizada no período de março, abril e maio de 2014, em cinco apiários localizados nos municípios de Condado, Pombal, Jericó, São Bentinho e São Domingos, ambos situados na Mesorregião do Sertão do Estado da Paraíba, com um total de 25 colmeias avaliadas. O teste de comportamento higiênico foi realizado com base no método de perfuração das células de crias. Para análise da infestação por ácaro Varroa destructor, as abelhas adultas foram coletadas e acondicionadas em frascos de vidro contendo álcool a 70%, após 24 horas foi realizada a contagem dos ácaros. Para a identificação de Nosema spp. foi utilizado o método de nível de infecção da colônia, sendo selecionadas dez abelhas adultas para retirada do aparelho intestinal e posterior leitura em microscópio óptico. O percentual de comportamento higiênico foi semelhante em ambos os apiários, principalmente em Condado (93,96%), Pombal (94,30%), Jericó (87,63%) e São Domingos (95,20%), ocorrendo apenas uma ligeira diferença no apiário de São Bentinho com uma média de 76,31%. No apiário de Condado foram encontrados 59 ácaros Varroa destructor, nas colmeias do apiário de Pombal obteve-se apenas seis ácaros, em Jericó foram encontrados 19 ácaros, no apiário de São Bentinho, foi observado o maior número de ácaros entre os apiários, 62 varroas no total e no apiário de São Domingos foram observados 48 ácaros. A frequência de Nosema spp. em colmeias manejadas na região do estudo foi de 36% de um total de 25 enxames avaliados. O apiário localizado no município de Pombal obteve os melhores resultados, apresentando índice elevado de comportamento higiênico e valores baixos para varroatose e nosemose. A africanização das abelhas e as altas temperaturas do Sertão Paraibano tendem a possibilitar melhor resistência das colmeias a enfermidades. / Beekeeping is one of the few agricultural activities that meets the three requirements of sustainability: economic, social and ecological. Therefore, provides income for the beekeeper, occupies family labor or hired and contributes to the preservation of native flora. The bee is important for the world economy as a pollinator, increasing the production of fruits and seeds, and as a supplier of honey, beeswax, royal jelly, propolis, pollen and venom (bee venom). These products are in high demand for raw materials in the pharmaceutical, food and cosmetics industries and for consumption in natural form industries. Sanity may affect the development of beekeeping because Apis mellifera like any other living organism is susceptible to diseases caused by bacteria, viruses, fungi and other parasites and metabolic, nutritional and hormonal disorders, and several poisoning. Thus, because of the need for studies concerning the apiculture health, this study focuses on hygienic behavior and the identification of varroatose and nosemosis in Apis mellifera honeybee colonies in apiaries located in the backlands of the state of Paraíba. The survey was conducted from March, April and May 2014, in five apiaries located in the cities of Condado, Pombal, Jericó, São Bentinho and São Domingos, both located in the Greater Region of the Backlands of the State of Paraíba, with a total of 25 evaluated hives. The hygienic behavior test was conducted based on the method of drilling the brood. For analysis of the mite Varroa destructor infestation, the adult bees were collected and placed in glass vials containing 70% alcohol, after 24 hours was performed to count the mites. For the identification of Nosema spp. we used the colony of infection level method, selecting ten adult bees to remove the intestinal and later reading device in an optical microscope. The hygienic behavior percentage was similar in both apiaries, especially in Condado (93.96%), Pombal (94.30%), Jericó (87.63%) and São Domingos (95.20%); there was one slight difference in the apiary of São Bentinho with an average of 76.31%. In Condado apiary found 59 mites Varroa destructor in the apiary hives of Pombal was obtained only six mites Jericó found 19 mites in the apiary of São Bentinho, it was observed the largest number of mites between apiaries, 62 varroas in total and in the apiary of São Domingos were observed 48 mites. The frequency of Nosema spp. In the hives managed in the region of the study was 36% of a total of 25 clusters evaluated. Apiary located in the city of Pombal obtained the best results, with high level of hygienic behavior and low values for varroatose and nosemosis. The Africanization of bees and the high temperatures of the Paraiba’s hinterlands tend to provide improved resistance to diseases of the hives.

The thesis covers the term for specific data analysis called Big Data. The thesis firstly defines the term Big Data and the need for its creation because of the rising need for deeper data processing and analysis tools and methods. The thesis also covers some of the technical aspects of Big Data tools, focusing on Apache Hadoop in detail. The later chapters contain Big Data market analysis and describe the biggest Big Data competitors and tools. The practical part of the thesis presents a way of using Apache Hadoop to perform data analysis with data from Twitter and the results are then visualized in Tableau.

Traditional relational database systems can not be efficiently used to analyze data with large volume and different formats, i.e. big data. Apache Hadoop is one of the first open-source tools that provides a distributed data storage system and resource manager. The space of big data processing has been growing fast over the past years and many technologies have been introduced in the big data ecosystem to address the problem of processing large volumes of data, and some of the early tools have become widely adopted, with Apache Hive being one of them. However,with the recent advances in technology, there are other tools better suited for interactive analytics of big data, such as Apache Spark and Presto. In this thesis these technologies are examined and benchmarked in order to determine their performance for the task of interactive business intelligence queries. The benchmark is representative of interactive business intelligence queries, and uses a star-shaped schema. The performance HiveTez, Hive LLAP, Spark SQL, and Presto is examined with text, ORC, Parquet data on different volume and concurrency. A short analysis and conclusions are presented with the reasoning about the choice of framework and data format for a system that would run interactive queries on bigdata. / Traditionella relationella databassystem kan inte användas effektivt för att analysera stora datavolymer och filformat, såsom big data. Apache Hadoop är en av de första open-source verktyg som tillhandahåller ett distribuerat datalagring och resurshanteringssystem. Området för big data processing har växt fort de senaste åren och många teknologier har introducerats inom ekosystemet för big data för att hantera problemet med processering av stora datavolymer, och vissa tidiga verktyg har blivit vanligt förekommande, där Apache Hive är en av de. Med nya framsteg inom området finns det nu bättre verktyg som är bättre anpassade för interaktiva analyser av big data, som till exempel Apache Spark och Presto. I denna uppsats är dessa teknologier analyserade med benchmarks för att fastställa deras prestanda för uppgiften av interaktiva business intelligence queries. Dessa benchmarks är representative för interaktiva business intelligence queries och använder stjärnformade scheman. Prestandan är undersökt för Hive Tex, Hive LLAP, Spark SQL och Presto med text, ORC Parquet data för olika volymer och parallelism. En kort analys och sammanfattning är presenterad med ett resonemang om valet av framework och dataformat för ett system som exekverar interaktiva queries på big data.

The term “hive mind” was originally used to describe the behavior of social insectslike bees and ants. Later, writers began using it to describe malevolent collective intelligencethat eradicates individuality. However, with time, the meaning of the phrase becameambiguous as some later writers began using it in a neutral or positive sense. This paper aims to investigate the implications of the historical ambiguity of the term“hive mind” in the context of the dystopian novel. The paper goes through a detailed analysisof Aldous Huxley’s Brave New World and George Orwell’s 1984 to unravel the intricacies ofthe hive mind phenomenon.

Les années 1860-1880 forment une charnière dans l’histoire de la démocratie britannique, car cette période fut celle de la seconde loi de Réforme, qui étendit le droit de vote à de plus larges sections des classes ouvrières. Ce changement politique fut le fruit de nombreuses manifestations nationales dont les syndicats furent les figures de proue. L’engagement en faveur de l’Union au cours de la guerre de Sécession, et en faveur de l’unité nationale italienne et polonaise, fut le vecteur de l’entrée en politique des classes ouvrières. La construction de la citoyenneté ouvrière est étudiée ici au travers des articles du Bee-Hive, journal ouvrier radical et libéral qui fut créé en 1861 pour réclamer en priorité le droit de vote. / The 1860s-1870s form a turning point in the history of British democracy, because these were the years when the second Reform Act was passed, which extended the right to vote to larger sections of the working classes. Such political change was the result of numerous national demonstrations organised by the Trades’ Unions. By showing their strong commitment to the Union during the American Civil War, and to Italian and Polish national unity, the working classes signalled their determination to enter the field of politics. This doctoral thesis studies the construction of working class citizenship through the pages of the Bee-Hive, a radical and liberal working class paper primarily created in 1861 to claim for suffrage.

Many functions in today’s society are immensely dependent on data. Data drives everything from business decisions to self-driving cars to intelligent home assistants like Amazon Echo and Google Home. To make good decisions based on data, of which exabytes are generated every day, somehow that data has to be processed. Data processing can be complex and time-consuming. One way of reducing the complexity is to create workflows that consist of several steps that together produce the right result. Klarna is an example of a company that relies on workflows for transforming and analyzing data. As a company whose core business involves analyzing customer data, being able to do those analyses faster will lead to direct business value in the form of more well-informed decisions. The workflows Klarna use are currently all written in a sequential form. However, workflows, where independent tasks are executed in parallel, are more performant than workflows where only one task is executed at any point in time. Due to limitations in human attention span, parallelized workflows are harder for humans to write, compared to sequential workflows. In this work, a computer application was created that automates the parallelization of a workflow to let humans write sequential workflows while still getting the performance of parallelized workflows. The application does this by taking a simple sequential workflow, identifies dependencies in the workflow and then schedules it in a way that is as parallel as possible given the identified dependencies. Such a solution has not been created before. However, experimental evaluation shows that parallelization of a sequential workflow used in daily production at Klarna can reduce execution time by up to 80%, showing that the application can bring value to Klarna and other organizations that use workflows to analyze big data.

Diploma Thesis focuses on integrating Hadoop platform into current data warehouse architecture. In theoretical part, properties of Big Data are described together with their methods and processing models. Hadoop framework, its components and distributions are discussed. Moreover, compoments which enables end users, developers and analytics to access Hadoop cluster are described. Case study of batch data extraction from current data warehouse on Oracle platform with aid of Sqoop tool, their transformation in relational structures of Hive component and uploading them back to the original source is being discussed at practical part of thesis. Compression of data and efficiency of queries depending on various storage formats is also discussed. Quality and consistency of manipulated data is checked during all phases of the process. Fraction of practical part discusses ways of storing and capturing stream data. For this purposes tool Flume is used to capture stream data. Further this data are transformed in Pig tool. Purpose of implementing the process is to move part of data and its processing from current data warehouse to Hadoop cluster. Therefore process of integration of current data warehouse and Hortonworks Data Platform and its components, was designed

This thesis deals with the development of an affordable hive scale which will be placed under the hive. The scale is expected to show the user all necessary information via the GSM mobile network. Incoming SMS message informs the user about the current weight and moisture. The information obtained can be displayed graphically in the application for android phones.

The aim of this thesis was to monitor the Varroa destructor parasitic mite in correlation with the microclimatic conditions of the Carniolan honey bee (Apis mellifera carnica). The rate of infestation of selected bee colonies at different locations was assessed in three-day intervals. At the same time, the microclimate in the hives was observed. The monitoring took place from April to mid-October. Also, the effect of the microclimate on the mite fall count was evaluated. During the whole evaluation, the highest average daily fall count was 2.08 mites per day at honeybee colonies at location 1 and 2; 1.97 at location 3. There was no statistically significant difference (P > 0.05) between those locations. The comparison of fall count between the moths of observation revealed, that highest fall count was during September (3.03 mites per day) and the lowest fall count was in April (0.41 mites per day; P 0.001). The rates of dependence (assessed by correlation analysis) between the fall count and microclimatic conditions in individual colonies varied. The strongest correlation between hive temperature and fall count (r = -0.45, P 0.05) was found at location 2. A low correlation was found at location 1 (r = -0.17, P 0.05). On the other hand, location 3 showed an insignificant and inconclusive correlation between hive temperature and fall count (r = 0.003, P > 0.05). The aggregate data (without distinction of location or month) showed significant (P 0.05) correlation between fall count and hive temperature (r = -0.14). The correlation between relative air humidity in hive and the fall count was statistically insignificant and low (r = -0.02, P > 0.05). The results revealed that the degree of correlation between the hive microclimate and the development (fall count respectively) of the Varroa destructor population is different for each colony. The monitoring of the dead Varroa destructor females is an appropriate complementary tool to diagnose a colony's infestation. The statistical analysis confirmed that with the decreasing summer and end-of-summer temperatures the Varroa destructor population grows and it is necessary to take measures to suppress its growth due to the development of the honey bee long-term winter generation.

With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large data sets, collectively known as “big data”. The term “big-data” in this thesis refers to large, diverse, complex, longitudinal and/or distributed data sets generated from instruments, sensors, internet transactions, email, social networks, twitter streams, and/or all digital sources available today and in the future. We introduced architectures and concepts for implementing a cloud-based infrastructure for analyzing large volume of semi-structured and unstructured data. We built and evaluated an application prototype for collecting, organizing, processing, visualizing and analyzing data from the retail industry gathered from indoor navigation systems and social networks (Twitter, Facebook etc). Our finding was that developing large scale data analysis platform is often quite complex when there is an expectation that the processed data will grow continuously in future. The architecture varies depend on requirements. If we want to make a data warehouse and analyze the data afterwards (batch processing) the best choices will be Hadoop clusters and Pig or Hive. This architecture has been proven in Facebook and Yahoo for years. On the other hand, if the application involves real-time data analytics then the recommendation will be Hadoop clusters with Storm which has been successfully used in Twitter. After evaluating the developed prototype we introduced a new architecture which will be able to handle large scale batch and real-time data. We also proposed an upgrade of the existing prototype to handle real-time indoor navigation data.