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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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En gropkeramisk rundtur på Gotland : GIS-analyser av gropkeramiska lokaler på Gotland och osteologiska bedömningar av resursutnyttjande / A Pitted Ware round-trip on Gotland : GIS-analyses of Pitted Ware Culture sites on Gotland and osteological assessments of resource utilisationEriksson, Albin January 2019 (has links)
The aim of this master thesis is to expand on the understanding of the resource utilisation on the 19 Gotlandic Pitted Ware Culture sites: Ajvide, Alvena, Fridtorp, Grausne, Gullrum,Gumbalde, Hau, Hemmor, Hoburgen, Ire, Kinner/Tjauls, Rangvide, Barshalder, Stenstugu,Stora Förvar, Sudergårds II, Visby, Västerbjers and Västerbys. The study utilises theoretical frameworks such as Site Catchment Analysis, Site Territorial Analysis and Optimal ForagingTheory and is based on two main questions: Which animals did the diet on each site consist of? And are there any apparent connections between diet and topography/environment? To answer these questions, osteological records have been studied to get an idea of the animal food resources utilised on each site. ArcGIS has also been used to create height- and soil maps with contemporary shorelines which show how the sites were located in the middle Neolithic Gotlandic landscape. The study has shown that most sites appear to have included a variety of animals like pig/boar, cattle, sheep/goat, fish, seal, porpoise and birds in their diet. The sites with the lowest number of confirmed animals also tend to have undergone the least archaeological investigation, suggesting that further excavations on these sites might unearth more animal species. Additional discoveries show a small albeit noticeable emphasis on marine animal resources, especially porpoise, on southern sites. Sites located in areas mostly consisting of sandy, meager soils also show an increased marine resource utilisation. This might suggest that the area around these sites were somewhat barren and lacking in terrestrial prey animals.
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Auto-Tuning Apache Spark Parameters for Processing Large Datasets / Auto-Optimering av Apache Spark-parametrar för bearbetning av stora datamängderZhou, Shidi January 2023 (has links)
Apache Spark is a popular open-source distributed processing framework that enables efficient processing of large amounts of data. Apache Spark has a large number of configuration parameters that are strongly related to performance. Selecting an optimal configuration for Apache Spark application deployed in a cloud environment is a complex task. Making a poor choice may not only result in poor performance but also increases costs. Manually adjusting the Apache Spark configuration parameters can take a lot of time and may not lead to the best outcomes, particularly in a cloud environment where computing resources are allocated dynamically, and workloads can fluctuate significantly. The focus of this thesis project is the development of an auto-tuning approach for Apache Spark configuration parameters. Four machine learning models are formulated and evaluated to predict Apache Spark’s performance. Additionally, two models for Apache Spark configuration parameter search are created and evaluated to identify the most suitable parameters, resulting in the shortest execution time. The obtained results demonstrates that with the developed auto-tuning approach and adjusting Apache Spark configuration parameters, Apache Spark applications can achieve a shorter execution time than when using the default parameters. The developed auto-tuning approach gives an improved cluster utilization and shorter job execution time, with an average performance improvement of 49.98%, 53.84%, and 64.16% for the three different types of Apache Spark applications benchmarked. / Apache Spark är en populär öppen källkodslösning för distribuerad databehandling som möjliggör effektiv bearbetning av stora mängder data. Apache Spark har ett stort antal konfigurationsparametrar som starkt påverkar prestandan. Att välja en optimal konfiguration för en Apache Spark-applikation som distribueras i en molnmiljö är en komplex uppgift. Ett dåligt val kan inte bara leda till dålig prestanda utan också ökade kostnader. Manuell anpassning av Apache Spark-konfigurationsparametrar kan ta mycket tid och leda till suboptimala resultat, särskilt i en molnmiljö där beräkningsresurser tilldelas dynamiskt och arbetsbelastningen kan variera avsevärt. Fokus för detta examensprojekt är att utveckla en automatisk optimeringsmetod för konfigurationsparametrarna i Apache Spark. Fyra maskininlärningsmodeller formuleras och utvärderas för att förutsäga Apache Sparks prestanda. Dessutom skapas och utvärderas två modeller för att söka efter de mest lämpliga konfigurationsparametrarna för Apache Spark, vilket resulterar i kortast möjliga exekveringstid. De erhållna resultaten visar att den utvecklade automatiska optimeringsmetoden, med anpassning av Apache Sparks konfigurationsparameterar, bidrar till att Apache Spark-applikationer kan uppnå kortare exekveringstider än vid användning av standard-parametrar. Den utvecklade metoden för automatisk optimering bidrar till en förbättrad användning av klustret och kortare exekveringstider, med en genomsnittlig prestandaförbättring på 49,98%, 53,84% och 64,16% för de tre olika typerna av Apache Spark-applikationer som testades.
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