Global ETD Search

101	Implementation och utvärdering av fika-applikation : En Design Science Research studie / Implementation and evaluation of a Fika-application : A Design Science Research study Lindblad, Adam January 2023 (has links) På företaget AFRY i Karlstad har en medarbetare med fika varje fredag och turordningen för detta sker i dagsläget med en Excel-fil vilket är problematiskt då inte alla har tillgång till filen och kan då inte se när det är deras tur att ta med fika, vilket resulterar i att det glöms av att ta med fika. Författaren har fått i uppdrag av AFRY att ta fram en webbaserad applikation där personalen kan se schemat för vems tur det är att ta med fika. En kravspecifikation upprättades av uppdragsgivaren tillsammans med en lista på den tekniska uppsättningen för webbapplikationen som gäller. Studiens syfte är att undersöka vilka komplikationer som uppstår vid implementationen samt hur användarna mottar applikationen. För detta har två undersökningsfrågor upprättats som ligger till grund för studien: Vad resulterar utvärderingarna i och vilka förslag till förbättring mottogs? Vilka komplikationer uppstår vid implementationen och hur har dessa lösts? Design Science Research applicerades som forskningsstrategi för studien där momenten Specificera krav, Implementering och Utvärdering användes för att skapa artefakten som det studeras kring. En agil arbetsmetod låg till grund för att föra arbetet framåt där det utvecklades i tre sprintar. Artefakten utvärderades av testpersoner som var personal hos uppdragsgivaren och det genomfördes Black Box- och Manuella tester där testpersonerna fick uttrycka och reflektera kring implementationen som utförts. Data samlades in med hjälp av strukturerade intervjuer där författaren förde anteckningar på vad som uttrycktes. Utvärderingarna resulterade i att det som implementerades saknade funktionalitet som testpersonerna efterfrågade. Flertalet data samlades in som påvisar att de krav som ställdes skulle behövt specificerats ytterligare för att uppnå högre tillfredsställelse från testpersonerna. De komplikationer som påträffades under implementationen kunde antingen lösas genom att läsa dokumentationen för teknik som användes eller att testpersonerna, som innehar mer erfarenhet än författaren, kunde lösa dessa. Slutsatsen för studien är att krav som ställs måste specificeras mer detaljrikt för att artefakten skall bli så likt användarnas bild av den som möjligt. Design Science Research Azure Active Directory Entity Framework Core LINQ Aurelia Utveckling Webbapplikation Information Systems, Social aspects
102	Low-No code Platforms for Predictive Analytics Karmakar, Soma January 2023 (has links) In the data-driven landscape of modern business, predictive analytics plays a pivotal role inanticipating and mitigating customer churn—a critical challenge for organizations. However, thetraditional complexities of machine learning hinder accessibility for decision-makers. EnterMachine Learning as a Service (MLaaS), offering a gateway to predictive modeling without theneed for extensive coding or infrastructure.This thesis presents a comprehensive evaluation of cloud-based and cloud-agonostic AutoML(Automated Machine Learning) platforms for customer churn prediction. The study focuses onfour prominent platforms: Azure ML, AWS SageMaker, GCP Vertex AI, and Databricks. Theevaluation encompasses various performance metrics including accuracy, AUC-ROC, precision,recall to assess the predictive capabilities of each platform. Furthermore, the ease of use andlearning curve for model development are compared, considering factors such as data preparation,training steps, and coding requirements. Additionally, model training times are analyzed toidentify platform efficiencies. Preliminary results indicate that AWS SageMaker exhibits thehighest accuracy, suggesting strong predictive capabilities. GCP Vertex AI excels in AUC,indicating robust discriminatory power. Azure ML demonstrates a balanced performance,achieving notable accuracy and AUC scores. Databricks being platform independent is a winnerand has also shown good metrics. Its capability to generate notebook is an added advantage whichcan be modified by experts to fine tune the results more. This research provides valuable insightsfor organizations seeking to implement different AutoML solutions for customer churnprediction. Low code no code Predictive analytics databricks azure AWS Google cloud Annan elektroteknik och elektronik
103	Autentiseringsprocesser i molnbaserade datortjänster Göthesson, Richard, Hedman, Gustav January 2016 (has links) Tidigare forskning har påvisat brister i olika former av autentiseringsprocesser som leder till autentiseringsattacker. Målet med vår studie är att presentera ett antal riktlinjer som företag och privatpersoner kan följa för att minimera risken för autentiseringsattacker. Metoderna som användes för att komma fram till dessa riktlinjer var kvalitativa där en praktisk observationsstudie, en litteraturstudie samt en enkätundersökning låg till grund för vår insamlade data. Resultatet av studien pekar på att Google Cloud Platform, Amazon Web Services och Microsoft Azure alla har en stark autentiseringsprocess i jämförelse med kritik från tidigare forskning. Enkätundersökningen visade dessutom att olika former av alternativ autentisering, såsom Two Factor Authentication (2FA) och Multi Factor Authentication (MFA), rekommenderas för ett starkt försvar mot autentiseringsattacker.Uppsatsens resultat pekar även på att användarens egenansvar i autentiseringsprocessen är av stor vikt för att minimera risken för autentiseringsattacker. Säkra lösenord bör konstrueras och frekvent bytas ut. Även alternativ autentisering och begränsning av användarens tillgång till känslig information bör tillämpas. / Previous research has shown deficiencies in various forms of authentication processes that lead to authentication attacks. The goal of our study is to present a number of guidelines that businesses and individuals can follow to minimize the risk of authentication attacks. The methods used to reach these guidelines were qualitative. They consisted of a practical observational study, a literature review and a survey, which formed the basis of our collected data. The results of the study indicate that Google Cloud Platform, Amazon Web Services and Microsoft Azure all have a strong authentication process in comparison with the criticism of previous research. The survey also showed that different forms of authentication methods, such as the Two Factor Authentication (2FA) and Multi Factor Authentication (MFA), are recommended for a strong defense against authentication attacks.The thesis’ results also points to the user’s own responsibility in the authentication process are essential to minimize the risk of authentication attacks. Secure passwords should be designed and frequently replaced. Alternative authentication and restricted access to sensitive information for the user should also be applied. molnbaserade datortjänster autentiseringsattacker autentisering Microsoft Azure Google Cloud Platform Amazon Web Services authentication cloud computing guidelines authentication attacks Engineering and Technology Teknik och teknologier
104	Cloud Computing Pricing and Deployment Efforts : Navigating Cloud Computing Pricing and Deployment Efforts: Exploring the Public-Private Landscape / Prissättning och Implementeringsinsatser för Molntjänster : Att Navigera Molntjänsters Prissättning och Implementeringsinsatser: Utforska det Offentlig-Privata Landskapet Kristiansson, Casper, Lundström, Fredrik January 2023 (has links) The expanding adoption of cloud computing services by businesses has transformed IT infrastructure and data management in the computing space. Cloud computing offers advantages such as availability, scalability, and cost-effectiveness, making it a favored choice for businesses of all sizes. The aim of this thesis is to compare private and public cloud computing services in terms of pricing and implementation effort as well as comparing the cloud providers to each other. The top three cloud providers that will be examined are Google GCP, Microsoft Azure, and Amazon AWS. The study examines different pricing models and evaluates their effectiveness in different business scenarios. In addition, the thesis also discusses the challenges associated with building and maintaining private infrastructure and the deployment of applications to cloud computing service are examined. The research methodology involves data collection, analysis, and a case study of developing and deploying a ticketing system application on different cloud platforms. The ticket system helps to provide a realistic example and investigation of the cloud providers. The findings will help companies make informed decisions regarding the selection of the most appropriate cloud computing service based on pricing models and implementation efforts. The thesis provides valuable information on private and public cloud computing and recommends appropriate pricing models for different scenarios. This study adds to existing knowledge by analyzing current pricing models and deployment concepts in cloud computing. The thesis does not propose new solutions but follows a structured format compiling information on private, and public cloud computing and a comprehensive review of cloud computing pricing models and marketing efforts. / Den växande adoptionen av molntjänster inom företag har förändrat IT-infrastrukturen och datahanteringen inom datorområdet. Molntjänster erbjuder fördelar såsom tillgänglighet, skalbarhet och kostnadseffektivitet, vilket gör det till ett populärt val för företag i alla storlekar. Syftet med denna avhandling är att jämföra privata och offentliga molntjänster med avseende på prissättning och implementeringsinsatser samt att jämföra molnleverantörerna med varandra. De tre främsta molnleverantörerna som kommer att undersökas är Google GCP, Microsoft Azure och Amazon AWS. Studien undersöker olika prismodeller och utvärderar deras effektivitet i olika affärsscenarier. Dessutom diskuterar avhandlingen också utmaningarna med att bygga och underhålla privat infrastruktur samt implementeringen av applikationer till molntjänster. Forskningsmetodologin omfattar datainsamling, analys och en fallstudie av utveckling och implementering av ett support system på olika molnplattformar. Supportsystemet hjälper till att ge ett realistiskt exempel och undersökning av molnleverantörerna. Resultaten kommer att hjälpa företag att fatta informerade beslut när det gäller valet av lämpligaste molntjänst baserat på prismodeller och implementeringsinsatser. Avhandlingen tillhandahåller värdefull information om privat och offentlig molntjänst och rekommenderar lämpliga prismodeller för olika scenarier. Denna studie bidrar till befintlig kunskap genom att analysera nuvarande prismodeller och implementeringskoncept inom molntjänster. Avhandlingen föreslår inga nya lösningar, men följer en strukturerad format genom att sammanställa information om privat och offentlig molntjänst samt en omfattande översikt av prismodeller och marknadsinsatser inom molntjänster. Cloud computing Private cloud Public cloud Cloud computing services Cost-effectiveness Implementation effort Google GCP Microsoft Azure Amazon AWS Pricing models Cloud adoption Cloud cost management Cloud migration Instance computing Serverless computing Data storage Molntjänster Privat moln Offentligt moln Kostnadsjämförelse Kostnadseffektivitet Google GCP Microsoft Azure Amazon AWS Molninförande Molnkostnadshantering Molnmigration Instance computing Serverless computing Dataförvaring Computer and Information Sciences Data- och informationsvetenskap
105	Mobile Cloud Computing: Offloading Mobile Processing to the Cloud Zambrano, Jesus 01 January 2015 (has links) The current proliferation of mobile systems, such as smart phones, PDA and tablets, has led to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device. However, this convergence penalizes the mobile system in computational resources such as processor speed, memory consumption, disk capacity, as well as in weight, size, ergonomics and the user’s most important component, battery life. Therefore, this current trend aims towards the efficient and effective use of its hardware and software components. Hence, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems. Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for off-loading computation and data processing from mobile devices restricted by reduced resources. In cloud computing, virtualization enables a logical abstraction of physical components in a scalable manner that can overcome the physical constraint of resources. This optimizes IT infrastructure and makes cloud computing a worthy cost effective solution. The intent of this thesis is to determine the types of applications that are better suited to be off-loaded to the cloud from mobile devices. To this end, this thesis quantitatively and qualitatively compares the performance of executing two different kinds of workloads locally on two different mobile devices and remotely on two different cloud computing providers. The results of this thesis are expected to provide valuable insight to developers and architects of mobile applications by providing information on the applications that can be performed remotely in order to save energy and get better response times while remaining transparent to users. Thesis University of North Florida UNF Dissertations Dissertations Academic -- UNF -- Computing Mobile Computing Cloud Computing Android Amazon Web Services Microsoft Windows Azure Mobile Energy Consumption Mobile Responde Time Computer Sciences
106	Empirical Performance Analysis of High Performance Computing Benchmarks Across Variations in Cloud Computing Mani, Sindhu 01 January 2012 (has links) High Performance Computing (HPC) applications are data-intensive scientific software requiring significant CPU and data storage capabilities. Researchers have examined the performance of Amazon Elastic Compute Cloud (EC2) environment across several HPC benchmarks; however, an extensive HPC benchmark study and a comparison between Amazon EC2 and Windows Azure (Microsoft’s cloud computing platform), with metrics such as memory bandwidth, Input/Output (I/O) performance, and communication computational performance, are largely absent. The purpose of this study is to perform an exhaustive HPC benchmark comparison on EC2 and Windows Azure platforms. We implement existing benchmarks to evaluate and analyze performance of two public clouds spanning both IaaS and PaaS types. We use Amazon EC2 and Windows Azure as platforms for hosting HPC benchmarks with variations such as instance types, number of nodes, hardware and software. This is accomplished by running benchmarks including STREAM, IOR and NPB benchmarks on these platforms on varied number of nodes for small and medium instance types. These benchmarks measure the memory bandwidth, I/O performance, communication and computational performance. Benchmarking cloud platforms provides useful objective measures of their worthiness for HPC applications in addition to assessing their consistency and predictability in supporting them. Thesis University of North Florida UNF Cloud Computing High Performance Computing HPC HPC Benchmarks Windows Azure Amazon EC2 Digital Communications and Networking Other Computer Engineering
107	Modelo tecnológico de análisis predictivo basado en machine learning para evaluación de riesgo crediticio Ortiz Huamán, Cesar Humberto, Haro Bernal, Brenda Ximena 15 July 2017 (has links) El incremento de herramientas e innovación en tecnología para la sociedad trae como resultado que las organizaciones empiecen a producir y almacenar grandes cantidades de datos. Así, la gestión y la obtención de conocimiento a partir de estos datos es un desafío y clave para generar ventaja competitiva. Dentro del proyecto dos enfoques son tomados en cuenta; la complejidad de implementación, los costos asociados por el uso de tecnologías y herramienta necesarias. Para encontrar los secretos que esconden los datos recolectados, es necesario tener una gran cantidad de ellos y examinarlos de forma minuciosa para así encontrar patrones. Este tipo de análisis es de complejidad alta para que nosotros mismos logremos detectar (Chappell & Associates, 2015). Campos de Ciencias de la Computación como Machine Learning servirán de base para la realización del análisis predictivo que permita anticiparnos al comportamiento futuro de las variables definidas según el problema que identifiquemos. El presente proyecto tiene como principio la necesidad de tener un Modelo Tecnológico de análisis predictivo basado en Machine Learning en la evaluación de riesgo crediticio. Fue tomada en consideración la situación actual sobre las diferentes implementaciones y arquitecturas que fueron desarrolladas por empresas que cuentan soluciones predefinidas o con propuestas generales que no permiten la flexibilidad y detalle de que necesita tener un sistema con la tecnología de Machine Learning. / Increasing tools and technology innovation for society results in organizations starting to produce and store large amounts of data. Thus, managing and obtaining knowledge from this data is a challenge and key to generating competitive advantage. Within this project two approaches are taken into account; The complexity of implementation and the costs associated with the use of necessary technologies and tools. To find the secrets that hide the collected data, it is necessary to have a large number of them and to examine them in order to find patterns. This type of analysis is highly complex so that we can detect it ourselves (Chappell & Associates, 2015). Fields of Computer Science as Machine Learning will serve as basis for the realization of the predictive analysis that allows us to anticipate the future behavior of the variables defined according to the problem that we identify. The present project has as principle the need to have a process model of predictive analysis based on machine learning for the evaluation of credit risk. It was taken into consideration the current situation regarding the different implementations and architectures that were developed by companies that have predefined solutions or with general proposals that do not allow the flexibility and detail that you need to have a system for the use of Machine Learning technology. / Tesis Riesgo crediticio Calificación crediticia Crédito financiero Machine Learning Studio Machine Learning Azure Credit risk Credit rating Financial credit
108	A Qualitative Comparative Analysis of Data Breaches at Companies with Air-Gap Cloud Security and Multi-Cloud Environments T Richard Stroupe Jr. (17420145) 20 November 2023 (has links) <p dir="ltr">The purpose of this qualitative case study was to describe how multi-cloud and cloud-based air gapped system security breaches occurred, how organizations responded, the kinds of data that were breached, and what security measures were implemented after the breach to prevent and repel future attacks. Qualitative research methods and secondary survey data were combined to answer the research questions. Due to the limited information available on successful unauthorized breaches to multi-cloud and cloud-based air gapped systems and corresponding data, the study was focused on the discovery of variables from several trustworthily sources of secondary data, including breach reports, press releases, public interviews, and news articles from the last five years and qualitative survey data. The sample included highly trained cloud professionals with air-gapped cloud experience from Amazon Web Services, Microsoft, Google and Oracle. The study utilized unstructured interviews with open-ended questions and observations to record and document data and analyze results.</p><p dir="ltr">By describing instances of multi-cloud and cloud-based air gapped system breaches in the last five years this study could add to the body of literature related to best practices for securing cloud-based data, preventing data breach on such systems, and for recovering from breach once it has occurred. This study would have significance to companies aiming to protect secure data from cyber attackers. It would also be significant to individuals who have provided their confidential data to companies who utilize such systems. In the primary data, 12 themes emerged. The themes were Air Gap Weaknesses Same as Other Systems, Misconfiguration of Cloud Settings, Insider Threat as Attack Vector, Phishing as Attack Vector, Software as Attack Vector, and Physical Media as Attack Vector, Lack of Reaction to Breaches, Better Authentication to Prevent Breaches, Communications, and Training in Response to Breach, Specific Responses to Specific Problems, Greater Separation of Risk from User End, and Greater Separation of Risk from Service End. For secondary data, AWS had four themes, Microsoft Azure had two, and both Google Cloud and Oracle had three.</p> Cloud computing air-gapped systems cloud-based air-gapped systems cybersecurity cybersecurity breach multi-cloud environments security measures cybersecurity attack Amazon Web Services (AWS) Google Cloud Platform Microsoft Azure Cloud oracle cloud infrastructure
109	Introducing Generative Artificial Intelligence in Tech Organizations : Developing and Evaluating a Proof of Concept for Data Management powered by a Retrieval Augmented Generation Model in a Large Language Model for Small and Medium-sized Enterprises in Tech / Introducering av Generativ Artificiell Intelligens i Tech Organisationer : Utveckling och utvärdering av ett Proof of Concept för datahantering förstärkt av en Retrieval Augmented Generation Model tillsammans med en Large Language Model för små och medelstora företag inom Tech Lithman, Harald, Nilsson, Anders January 2024 (has links) In recent years, generative AI has made significant strides, likely leaving an irreversible mark on contemporary society. The launch of OpenAI's ChatGPT 3.5 in 2022 manifested the greatness of the innovative technology, highlighting its performance and accessibility. This has led to a demand for implementation solutions across various industries and companies eager to leverage these new opportunities generative AI brings. This thesis explores the common operational challenges faced by a small-scale Tech Enterprise and, with these challenges identified, examines the opportunities that contemporary generative AI solutions may offer. Furthermore, the thesis investigates what type of generative technology is suitable for adoption and how it can be implemented responsibly and sustainably. The authors approach this topic through 14 interviews involving several AI researchers and the employees and executives of a small-scale Tech Enterprise, which served as a case company, combined with a literature review. The information was processed using multiple inductive thematic analyses to establish a solid foundation for the investigation, which led to the development of a Proof of Concept. The findings and conclusions of the authors emphasize the high relevance of having a clear purpose for the implementation of generative technology. Moreover, the authors predict that a sustainable and responsible implementation can create the conditions necessary for the specified small-scale company to grow. When the authors investigated potential operational challenges at the case company it was made clear that the most significant issue arose from unstructured and partially absent documentation. The conclusion reached by the authors is that a data management system powered by a Retrieval model in a LLM presents a potential path forward for significant value creation, as this solution enables data retrieval functionality from unstructured project data and also mitigates a major inherent issue with the technology, namely, hallucinations. Furthermore, in terms of implementation circumstances, both empirical and theoretical findings suggest that responsible use of generative technology requires training; hence, the authors have developed an educational framework named "KLART". Moving forward, the authors describe that sustainable implementation necessitates transparent systems, as this increases understanding, which in turn affects trust and secure use. The findings also indicate that sustainability is strongly linked to the user-friendliness of the AI service, leading the authors to emphasize the importance of HCD while developing and maintaining AI services. Finally, the authors argue for the value of automation, as it allows for continuous data and system updates that potentially can reduce maintenance. In summary, this thesis aims to contribute to an understanding of how small-scale Tech Enterprises can implement generative AI technology sustainably to enhance their competitive edge through innovation and data-driven decision-making. UX HCD HCI AI Artificial Intelligence Generative AI Implementing AI LLM GPT RAG Chatbot Prompt engineering Prompting framework Hallucinations Transparency Interaction Technologies Usability Linguistics Innovation SME Tech Enterprise Qte Growth factors Documentation Communication Knowledge retention Competence development Education Training Retrieval Model Data Data Management Database Knowledge base Maintenance Data security Data driven decision making Data analytics Social sustainability Economical sustainability Figma Trello Github OpenAI GTPs VsCode Azure CoPilot Google Bard Zapier GCS API Pipeline Parsing data Sorting data Storing data Human Computer Interaction Interaction Technologies Interaktionsteknik

Search results