Container technologies are rapidly changing how distributed applications are executed and managed on cloud computing resources. As containers can be deployed on a large scale, there is a tremendous need for Container Orchestration tools like Kubernetes that are highly automatic in deployment, scaling, and management. In recent times, the adoption of these container technologies like Docker has seen a rise in internal usage, commercial offering, and various application fields ranging from High-Performance Computing to Geo-distributed (Edge or IoT) applications. Big Data analytics is another field where there is a trend to run applications (e.g., Apache Spark) as containers for elastic workloads and multi-tenant service models by leveraging various container orchestration tools like Kubernetes. Despite the abundant research on the performance impact of containerizing big data applications, to the best of our knowledge, the studies that focus on specific aspects like scalability and resource management are largely unexplored, which leaves a research gap to study upon. This research studies the performance impact of autoscaling a big data analytics application on Kubernetes based on autoscaling mechanisms like Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). These state-of-art autoscaling mechanisms available for scaling containerized applications on Kubernetes and the available big data benchmarking tools for generating workload on frameworks like Spark are identified through a literature review. Apache Spark is selected as a representative big data application due to its ecosystem and industry-wide adoption by enterprises. In particular, a series of experiments are conducted by adjusting resource parameters (such as CPU requests and limits) and autoscaling mechanisms to measure run-time metrics like execution time and CPU utilization. Our experiment results show that while Spark performs better execution time when configured to scale with VPA, it also exhibits overhead in CPU utilization. In contrast, the impact of autoscaling big data applications using HPA adds overhead in terms of both execution time and CPU utilization. The research from this thesis can be used by researchers and other cloud practitioners, using big data applications to evaluate autoscaling mechanisms and derive better performance and resource utilization.

Im Kontext der Hochschul- und Bildungsforschung wird Evaluation in ihrer Gesamtheit als Steuerungs- und Controlling-Instrument eingesetzt, um unter anderem Aussagen zur Qualität von Lehre, Forschung und Administration zu liefern. Auch wenn der Qualitätsbegriff an den Hochschulen bislang noch immer sehr unterschiedlich geführt wird, verfolgen die Beteiligten ein einheitliches Ziel – die Evaluation als zuverlässiges (internes) Präventions- und VorhersageInstrument in den Hochschulalltag zu integrieren. Dass dieses übergeordnete Ziel mit einigen Hürden verbunden ist, liegt auf der Hand und wird in der Literatur bereits vielfältig diskutiert (Benneworth & Zomer 2011; Kromrey 2001; Stockmann & Meyer 2014; Wittmann 2013). Die Evaluationsforschung bietet einen interdisziplinären Forschungszugang. Instrumente und Methoden aus unterschiedlichen (sozialwissenschaftlichen) Disziplinen, die sowohl qualitativer als auch quantitativer Natur sein können, kommen zum Einsatz. Mixed Method/Multi Data–Ansätze gelten dabei – trotz des unstreitbar höheren Erhebungs- und Verwertungsaufwandes – als besonders einschlägig in ihrer Aussagekraft (Döring 2016; Hewson 2007). Allerdings finden (Big) Data Analytics, Echtzeit- und Interaktionsanalysen nur sehr langsam einen Zugang zum nationalen Hochschul- und Bildungssystem. Der vorliegende Beitrag befasst sich mit der Bedeutung von (Big) Data Analytics in der Evaluation. Zum einen werden Herausforderungen und Potentiale aufgezeigt – zum anderen wird der Frage nachgegangen, wie es gelingen kann, (soziale) Daten (automatisiert) auf unterschiedlichen Aggregationsebenen zu erheben und auszuwerten. Es werden am Fallbeispiel der Evaluation von E-Learning in der Hochschullehre geeignete Erhebungsmethoden, Analyseinstrumente und Handlungsfelder vorgestellt. Die Fallstudie wird dabei in den Kontext der Computational Social Science (CSS) überführt, um einen Beitrag zur Entwicklung der Evaluationsforschung im Zeitalter von Big Data und sozialen Netzwerken zu leisten.

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In recent years, there is an increased focus on integrating digital technologies in industrial processes, also termed ”Industry 4.0”. Out of the many challenges for the transition, one is to understand how to find useful insights from data collected over large periods of time, predominantly in industrial IT systems. Automotive assembly plant X is currently undergoing a digital transformation to leverage such technologies. There is an emphasis to understand the implications of data analytics and visualization and how it could be leveraged for process optimization. The torque tightening assembly process at plant X was chosen to carry out the study as there were opportunities to access the process data from the tool management system database. The purpose of this master thesis was thus to find the implications of data analytics on the torque tightening operations in assembly plant X. In addition, the thesis also aimed to understand how visualization of key performance indicators (KPIs) can improve traceability of operational deviations. In other words, the study aims to validate how data analytics and visualization of KPIs facilitate data-driven decision making, improve traceability of operational deviations. The research is based on an inductive, exploratory case study approach. The study was carried out by understanding the current state through a series of interviews and then followed by the development of the framework and dashboard for visualization of operational deviations. Further, a discussion on how data analytics and visualization could help in decision-making for continuous improvement efforts is presented. / På senare år har det funnits ett ökat fokus på att integrera digital teknik i industriella processer, även kallad ”Industry 4.0”. Av de många utmaningarna för övergången är en att förstå hur man kan hitta användbara insikter från data som samlats in under långa tidsperioder, främst i industriella IT-system. Fordonsmonteringsfabrik X genomgår för närvarande en digital transformation för att dra nytta av sådan teknik. Det finns en betoning på att förstå implikationerna av dataanalys och visualisering och hur det kan utnyttjas för processoptimering. Vridmoment åtdragning monteringsprocessen vid anläggning X valdes för att genomföra studien eftersom det fanns möjligheter att komma åt processdata från verktygshanteringssystemdatabasen. Syftet med detta examensarbete var alltså att hitta implikationerna av dataanalys på momentåtdragningsoperationerna i monteringsanläggning X. Dessutom syftade examensarbetet också till att förstå hur visualisering av nyckeltalsindikatorer (KPI) kan förbättra spårbarheten av driftsavvikelser. Med andra ord syftar studien till att validera hur dataanalys och visualisering av KPI:er underlättar datadrivet beslutsfattande, förbättrar spårbarheten av operativa avvikelser. Forskningen bygger på en induktiv utforskande fallstudiemetod. Studien genomfördes genom att förstå nuläget genom en serie intervjuer och sedan följdes av utvecklingen av ramverket och digital informationstavla för visualisering av operativa avvikelser. Vidare presenteras en diskussion om hur dataanalys och visualisering kan hjälpa till vid beslutsfattande för ständiga förbättringsarbeten.

According to the Centre for Research on the Epidemiology of Disasters, the global average number of CID has tripled in less than four decades (from approximately 1,300 Climate-Induced Disasters (CID) between 1975 and 1984 to around 3,900 between 2005 and 2014). In addition, around 1 million deaths and $1.7 trillion damage costs were attributed to CID since 2000, with around $210 billion incurred only in 2020. Consequently, the World Economic Forum identified extreme weather as the top ranked global risk in terms of likelihood and among the top five risks in terms of impact in the last 4 years. These risks are not expected to diminish as: i) the number of CID is anticipated to double during the next 13 years; ii) the annual fatalities due to CID are expected to increase by 250,000 deaths in the next decade; and iii) the annual CID damage costs are expected to increase by around 20% in 2040 compared to those realized in 2020. Given the anticipated increase in CID frequency, the intensification of CID impacts, the rapid growth in the world’s population, and the fact that two thirds of such population will be officially living in urban areas by 2050, it has recently become extremely crucial to enhance both community and city resilience under CID. Resilience, in that context, refers to the ability of a system to bounce back, recover or adapt in the face of adverse events. This is considered a very farfetched goal given both the extreme unpredictability of the frequency and impacts of CID and the complex behavior of cities that stems from the interconnectivity of their comprising infrastructure systems. With the emergence of data-driven machine learning which assumes that models can be trained using historical data and accordingly, can efficiently learn to predict different complex features, developing robust models that can predict the frequency and impacts of CID became more conceivable. Through employing data analytics and machine learning techniques, this work aims at enhancing city resilience by predicting both the occurrence and expected impacts of climate-induced disasters on urban areas. The first part of this dissertation presents a critical review of the research work pertaining to resilience of critical infrastructure systems. Meta-research is employed through topic modelling, to quantitatively uncover related latent topics in the field. The second part aims at predicting the occurrence of CID by developing a framework that links different climate change indices to historical disaster records. In the third part of this work, a framework is developed for predicting the performance of critical infrastructure systems under CID. Finally, the aim of the fourth part of this dissertation is to develop a systematic data-driven framework for the prediction of CID property damages. This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience. / Thesis / Doctor of Philosophy (PhD)

Recent years have seen an increase in use of the stream processing architecture to compose continuous analytics applications. This thesis presents the design of a Rust-based stream processor that adopts two separate techniques to tackle existing weaknesses in modern production-grade stream processors. The first technique employs a data analytics language on top of the streaming runtime, in order to provide both dataflow and low-level compiler optimisations. This technique is motivated by an analysis of the impact that the lack of compiler integration may have on the end-to-end performance of streaming pipelines in Apache Flink. In the second technique streaming operators are scheduled using a task-parallel approach to boost performance for skewed data distributions. The experimental results for data-parallel streaming pipelines in this thesis demonstrate, that the scheduling model of the prototype achieves performance improvements in skewed scenarios without exhibiting any significant losses in performance during uniform distributions. / Under senare år har användningen av strömbearbetningsarkitekturen ökat för att komponera kontinuerliga analysapplikationer. Denna avhandling presenterar designen av en Rust-baserad strömprocessor som använder två separata tekniker för att hantera befintliga svagheter i moderna strömprocessorer. Den första tekniken använder ett dataanalysspråk ovanpå strömprocessorn, för att ge både dataflöde och kompilatoroptimeringar på låg nivå. Denna teknik är motiverad av en analys av påverkan som bristen på kompilatorintegration kan ha på den slutliga prestandan för analysapplikationer i Apache Flink. I den andra tekniken schemaläggs strömningsoperatörer med hjälp av en uppgiftsparallell metod för att öka prestanda för skev datadistribution. De experimentella resultaten för data-parallella analysapplikationer i denna avhandling visar att schemaläggningsmodellen för prototypen uppnår prestandaförbättringar i ojämna distributioner utan att uppvisa några betydande förluster i prestanda under enhetliga fördelningar.

Big Data analytics är en fascinerande process som lyfter fram möjligheten att utvinna djupgående insikter och dra slutsatser från omfattande datamängder, och dess tillämpningsområden sträcker sig över flera branscher och områden inklusive köksbranschen och marknadsföring som denna studie fokuserar på. Utmaningen att dra nytta av Big Data analytics för att förstå konsumentbeteenden och preferenser kan dock vara betydande, men när företag tillhandahåller de rätta resurserna, kan de på ett effektivt sätt optimera sina marknadsföringsstrategier. Denna studie syftade till att utforska vilka specifika kompetenser och förmågor företag behöver för att framgångsrikt hantera stora datamängder, samt hur Big Data analytics kan bidra till en effektivare marknadsföring inom köksbranschen. Genom att använda en induktiv forskningsansats och genomföra semistrukturerade intervjuer med fyra företag inom den svenska köksbranschen, kopplades svaren till ett teoretiskt ramverk för att analysera datan och identifiera kompetenser, förmågor och effekter inom Big Data analytics. Resultaten visade på vikten av olika kompetenser såsom teknisk kunskap, datavisualisering, machine learning, marknadsföringskunskaper, kreativt tänkande, ledarskap och kommunikation för att uppnå framgång inom Big Data analytics. Studien betonade också den avgörande betydelsen av att samla in högkvalitativ data, integrera verktyg och använda insikterna för att optimera marknadsföringsstrategier inom köksbranschen. Som förslag till framtida forskning framhåller denna studie behovet av att undersöka organisatoriska och kulturella faktorer som påverkar potentialen inom Big Data analytics. Det föreslås även att framtida forskning bör utforska andra branscher och deras specifika behov när det gäller Big Data analytics, vilket kan bredda förståelsen inom detta område. Nyckelord: Big Data analytics, effektivare marknadsföring, kompetenser, Big Data analytics förmågor, dynamiska förmågor, marknadsrelaterade förmågor, materiella resurser, immateriella resurser, mänskliga förmågor / Summary Big Data analytics is a fascinating process that highlights the ability to extract profound insights and draw conclusions from large amounts of data. Its applications span across various industries and domains, including the kitchen industry and marketing, which this study focuses on. However, harnessing the power of Big Data analytics to understand consumer behavior and preferences can be challenging. Nevertheless, when companies provide the right resources, they can effectively optimize their marketing strategies. This study aimed to explore the specific competencies and skills that companies need to successfully handle large volumes of data, as well as how Big Data analytics can contribute to more efficient marketing in the kitchen industry. Employing an inductive research approach and conducting semi-structured interviews with four companies in the Swedish kitchen industry, the findings were linked to a theoretical framework to analyze the data and identify competencies, skills, and effects within Big Data analytics. The results underscored the importance of various competencies such as technical knowledge, data visualization, machine learning, marketing expertise, creative thinking, leadership, and communication in achieving success in Big Data analytics. The study also emphasized the crucial significance of collecting high-quality data, integrating tools, and utilizing insights to optimize marketing strategies within the kitchen industry. As a suggestion for future research, this study highlights the need to examine organizational and cultural factors that impact the potential of Big Data analytics. Additionally, it is proposed that future research should explore other industries and their specific requirements regarding Big Data analytics, thereby broadening the understanding in this field.

While many large firms have implemented Big Data Analytics (BDA), it is unclear whether Small and Medium-sized Enterprises (SMEs) are ready to adopt and use this technology. This study investigates BDA implementation from the perspective of both managers and data analysts. Managers are mostly influenced by factors from the external environment, while data analysts are mostly influenced by technological factors. Hence, in this study, it is contended that managers imitate the behavior of external institutions, while data analysts mostly evaluate technology characteristics in the process of BDA implementation. The present study draws on institutional, organizational change, and diffusion of innovation theories through the lens of an imitation-evaluation perspective to investigate readiness and adoption behaviours. Accordingly, a theoretical research model was developed to explore the salient variables that impact organizational and data analysts’ readiness for implementing BDA in SMEs. To test these assertions, two surveys were conducted with 340 responses including 170 managers and 170 data analysts in SMEs in North America. The findings demonstrate that: (1) an imitation perspective plays a significant role in organizational readiness to adopt BDA; (2) uncertainty in big data technologies can intensify the effect of normative pressures on organizational readiness; (3) big data complexity, trialability, and relative advantage impact data analysts’ readiness to use big data analytics; and (4) the influence of relative advantage is attenuated by the high level of data analytics skills. These findings provide valuable contributions to the theory and practice of BDA implementation in SMEs in the BDA adoption and use literature. / Dissertation / Doctor of Business Administration (DBA)