Recommendation systems serve to users of e-commerce applications for individual recommendations to certain products or services based on their preferences. The aim of this thesis is to create a module of recommender system. The work includes analysis of recommendation systems and the methods used in these systems, including a description of the calculations. This work also solves the cold start problem, which is a problem when generation of some good recommendations for the new user is needed, but the recommendation system has no or little information about this user. Based on analysis is in this thesis designed module for recommender system, which is applicable e.g. internet for e-commerce or other internet-based application. Part of this module is the realization of a platform Apache Mahout, which some parts are built on a distributed computing platform Apache Hadoop project. Furthermore, in this work, on the aforementioned platform Mahout, selected methods of calculating the similarity using selected criteria (e.g. the average time for a recommendation, and the number of users for who have not been able to generate recommendations) are tested.

The flexibility of future mobile networks exploiting modern technologies such as cloud-optimized radio access and software-defined networks opens a gateway to deploying dynamic strategies for live and on-demand content delivery. Traditional live broadcasting systems are spectral inefficient. It takes up a lot more radio spectrum than that of mobile networks, to cover the same size of an area. Furthermore, content caching at base stations reduces network traffic in core networks. However, numerous duplicated copies of contents are still transmitted in the unicast fashion in radio access networks. It consumes valuable radio spectrum and unnecessary energy. Finally, due to the present of numerous mobile receivers with a wide diversity of wireless channels in a base station coverage area, it is a challenge to select a proper modulation scheme for video broadcasting to optimize the quality of services for users. In this thesis, the challenges and the problems in the current strategies for content delivery are addressed. A holistic novel solution is proposed that considers user preferences, user mobility, device-to-device communication, physical-layer resource allocation, and video quality prediction. First, a system-level scheduling framework is introduced to increase the spectral efficiency on broadcasting live contents onto mobile networks. It considers the audience preferences for allocating radio resources spatially and temporally. Second, to reduce the redundant transmissions in radio access networks, a content distribution system that exploits user mobility is proposed that utilizes the urban-scale user mobility and broadcasting nature of wireless communication for delay-tolerant large size content. Third, to further reduce the energy consumption in network infrastructure, a content distribution system that relies on both user mobility, and device-to-device communication is proposed. It leverages the mobile users as content carriers to offload the heavy mobile traffic from network-level onto device-level. Fourth, to mitigate the multi-user channel diversity problem, a cross-layer approach is deployed to increase the video quality for users especially for those who have a low signal-to-noise ratio signal. Finally, data mining techniques are employed to predict video qualities of wireless transmissions over mobile networks. The holistic solution has been empirically developed and evaluated. It achieves high spectral and energy efficiency and mitigates the video quality degradation in mobile networks.

En raison du grand succès des réseaux sans _l et des appareils portatifs, le paradigme de l'informatique pervasive est devenu une réalité. L'un des plus di_ciles objectifs à atteindre dans de tels environnements est de permettre à l'utilisateur d'exécuter une tâche en composant à la volée, les services et les ressources de l'environnement. Cela implique la correspondance et la sélection automatique de services à travers divers dispositifs de l'environnement pervasif. Les approches existantes considèrent souvent seulement les aspects fonctionnels des services et ne prennent pas en compte diff érents aspects non-fonctionnels tels que les préférences utilisateur, les capacités des dispositifs en termes matériels et logiciels, et l'hétérogénéité du réseau de ces dispositifs. Nous présentons une approche pour la sélection dynamique des composants et des dispositifs dans un environnement pervasif en considérant simultanément tous les aspects précédemment mentionnés. Premièrement, nous proposons une modélisation abstraite et concrète de l'application, des capacités des terminaux et des ressources, des préférences des utilisateurs, ainsi que la modélisation de la plate-forme réseau sous-jacente. Les capacit és des dispositifs sont représentées par notre extension du modèle CC/PP et les préférences des utilisateurs en utilisant notre extension du modèle CP-Net. Nous mod- élisons sous forme d'un graphe la tâche de l'utilisateur et des services réseau sous-jacent, ainsi que les exigences des services, des préférences utilisateur et les capacités des dispositifs. L'hétérogénéité des protocoles de communication est également considérée dans les graphes. Les aspects algorithmiques ont été traités en fournissant des algorithmes pour la correspondance entre les services et les composants, pour la projection des applications sur la plate-forme de composants existants et pour l'évaluation des préférences utilisateurs. Pour la description de la composition de l'application nous proposons un modèle SCA étendu. Partant d'une composition abstraite de services, nous arrivons à réaliser une composition concrète de l'application distribuée à travers les dispositifs existants. Si pendant l'exécution un nouveau meilleur dispositif apparaît, l'application est recomposée en tenant compte des nouveaux composants. Cela permet de réaliser la continuité de la session d'un dispositif vers un autre. Une mise en oeuvre d'un prototype et son évaluation sont également fournis. / Due to the large success of wireless networks and portable devices, the pervasive computing paradigm is becoming a reality. One of the most challenging objectives to be achieved in pervasive computing environments is to allow a user to perform a task by composing on the _y the environment's service and resource components. This involves automatic matching and selection of services across various devices in the pervasive environment. Existing approaches mostly consider only functional aspects for service and component matching and do not consider various non-functional aspects such as user preferences, device capabilities in terms of software and hardware, and network heterogeneity of devices. We present an approach for dynamic selection of components and devices in a pervasive environments considering all the aforementioned aspects simultaneously. First, we provide a modeling of abstract and concrete application, device capabilities and resources, user preferences as well as modeling of the underlying connected platform. Device capabilities are represented by our extended CC/PP model and user preferences using our extended CP-net model. We model both the user task and the underlying network services, along with service requirements, user preferences and device capabilities, as graphs. The heterogeneity of communication protocols is also considered in the graph. The algorithmic aspects have been treated by providing algorithms for service and component matching, application mapping on network platform and user preference evaluation. For description of application composition extended SCA model is used. Departing from an abstract composition, we arrive on achieving a concrete application composition which may be distributed across more than one device. If during the application execution a new, better device appears, the application is recomposed to replace the existing components by the newer ones. This also implies the continuity of session from one device to another. A prototype implementation and its evaluation are also provided.

This thesis analyzes the relationship between users&rsquo / characteristics and users&rsquo / interface preferences. An online survey is developed for this study. This survey composed of two types of questions: (1) users&rsquo / personal information such as age, gender, country, cognitive structure, and also computer experience and (2) user interface elements. More than 2,500 participants from 120 different countries throughout the world completed our survey. Results were analyzed using cross tables. Our findings show that there is a relationship between users&rsquo / characteristics and users&rsquo / interface preferences. In the presence of this relationship, an artificial neural network model is developed for the estimation of the interface preferences based on the user characteristics.

Effekterna av covid-19 pandemin har ytterligare accelererat den utveckling som skett i samhället under en längre tid. Allt mer av den privata och yrkesmässiga vardagen sker numera digitalt, där distansarbete blivit normen för många. Arbetslivet är möjligen det område som har påverkats allra mest av pandemin och medfört att en stor del av företagen tvingats ställa om sin verksamhet. Den ökade digitaliseringen har gjort att människor kan arbeta från i stort sett var som helst i världen och att en fysisk arbetsplats inte längre är fullkomligt nödvändig. När många företag skär ned på sina kontorslokaler och uppmanar till ökad flexibilitet, så kvarstår dock fortfarande behovet av att ha en fast punkt och en plats där socialt utbyte sker. Coworking spaces är en kontorsform som växt sig allt starkare i takt med detta eftersom det möjliggör både flexibilitet och social interaktion i ett paket. Flera undersökningar pekar nu mot att efterfrågan och tillväxten på coworking spaces kommer att vara hög framöver. Samtidigt är detta ett relativt outforskat fenomen och det saknas studier om vad som gör dessa framgångsrika. Syftet med denna studie är därför att öka förståelsen för hur coworking spaces kan stärka sitt värdeerbjudande, och därigenom attrahera fler användare. Mer specifikt är studien avgränsad till att studera coworking spaces i Västerbotten, vilket bidrar till att nytt geografiskt område undersöks. Genom kvalitativa intervjuer med såväl användare som värdar, ämnar studien bidra med stöd och vägledning för coworkingföretagen. Resultatet visar att företagens nuvarande värdeerbjudande till viss del skiljer sig från användarnas preferenser, vilket kan vara både intressant och insiktsfullt för företagen. Vidare visar resultatet att flertalet aspekter värdesätts annorlunda i jämförelse med tidigare studier, vilket tyder på att användarnas preferenser kan skilja sig världen över. För coworkingföretagen blir det därför viktigt att fortsätta studera användarnas preferenser i den miljön de verkar i, för att på så sätt utveckla sitt värdeerbjudande än mer framöver. En del i utvecklingen bör vara att sprida konceptet coworking och medvetengöra fler personer om dess värde och aktualitet i den allt mer flexibla verkligheten. Genom att göra detta finns det goda möjligheter till att fler personer väljer att gå den vägen och göra coworking spaces till sin arbetsplats i framtiden.

Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach. / Doctor of Philosophy / Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. When electricity demand is high, Demand Response (DR) is a method that can be used to reduce user loads, restoring the balance between demand and supply of electricity. Based on data from the Energy Information Administration (EIA), half of all commercial buildings in the US measure 5,000 square feet or smaller in size, whereas the majority of the other half is made up of medium-sized commercial buildings measuring in at between 5,001 to 50,000 square feet. This makes these commercial buildings an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique, where power consumption and activity of individual HVACs can be obtained, using a single power meter. The second work focuses on a DR algorithm, that controls single floor HVAC and lighting loads in a commercial building, based on a user generated bid price for electricity, user preferences and load priorities, when electricity demand is at its peak.

Background: Digital platforms include various business models and represent a large and growing part of the economy empowered by digitalization. The added value of platforms depends crucially on their user interactions, which is why adopting new users is crucial. In the internationalization process, digital platforms differ from traditional companies because it is not their own presence that matters, but the users’ location.   Problem: To join a digital platform, the size of the existing user base has been discussed as a decisive feature by the literature. This assumption is based on the concept of network effects. However, these can lose their effect when national borders are crossed and/or digital platforms are location-bound. In the overall discussion about digital platforms, the relevant sub-category of digital marketplaces, especially in the B2B area, has not yet been taken into account and no alternatives for the size of the existing user base have been discussed.   Purpose: This study aims to investigate the attractiveness of digital platform attributes for potential international users of digital B2B marketplaces when the latter enter a foreign market.   Research Question: Which attributes and which of their levels do potential users prefer in a cross-border digital B2B marketplace?   Method: The study is a quantitative, inductive research with an exploratory purpose. An internal realist ontology and a positivist epistemology are applied. Data are collected and analyzed by using the traditional rating-based conjoint analysis. Four attributes with two times two and two times three levels are queried in nine company profiles using an Internet survey questionnaire with n = 159.    Conclusion: This study extends the existing literature on digital platforms by capturing identifying characteristics of digital B2B marketplaces and exploring potential users' preferences for such platforms. We conclude a high importance of distance to users who are already active on the platform, as well as the language of the platform. This is followed by the location of active users and rounded off with a lower importance of user verification.

Biometric systems have become increasingly popular for user authentication andidentification across various domains, including smartphones, laptops, financial services,healthcare, and security. These systems offer enhanced security and quickaccess to data, aiming to address the challenges associated with passwords and pins.However, achieving a method that provides a 100% guarantee in all fields and for alldevices remains a challenge. To explore user perceptions on the acceptance of biometricsystems, an online survey was conducted with 99 participants from diversebackgrounds, education levels, ages, and countries. The survey focused on understandingusers’ acceptance of biometric systems based on their experiences and perspectives.Additionally, it aimed to investigate whether demographic factors, suchas age, education, and background, influence user acceptance. The study also comparedthe preferred authentication technique among users with findings from previousstudies. The survey results supported previous research, showing that fingerprinttechnology is the most recommended and preferred method for user authentication,followed by facial recognition. This study sheds light on the growing adoption ofbiometric systems to overcome password-related issues and provides valuable insightsinto user preferences for authentication and identification methods.

Computing is moving away from the desktop, permeating into many everyday objects and the environments in which we live. Many researchers in ubiquitous computing are excited about the potential to profoundly change the way we live by revolutionizing how we interact with information. Despite the excitement, few successful applications are making the transition from the laboratories to the mass market. While this could easily be attributed to the immaturity of the research area, it is also a manifestation of a larger problem—the lack of coherent methods, processes or tools that assist designers in thinking about issues pertinent to ubiquitous computing, as they explore potential ideas and develop some of these into working prototypes. To this end, this research presents an overview of the important characteristics of ubiquitous computing systems identified by many of the leading researchers in the field. Contrasting with conventional systems, we discuss the resulting issues and challenges, and their implications on the future directions of this emerging research area. In a case study, we use scenario-based design to walkthrough the design of a community computing application. At various stages of the design process, the need to focus on more issues relevant to ubiquitous computing design became apparent, resulting in the augmentation of scenario-based design. The augmented scenario-based design process is proposed as a tool for helping designers conceptualize user activities within given usage circumstances and at various stages of the design process. New questions help to identify the most common pitfalls, enabling designers to produce systems that are more socially acceptable and provide a higher likelihood for adoption by everyday users beyond the laboratory. In initial testing, the augmented process was shown to produce better designs. The ultimate ambition of ubiquitous computing technology is to be able to serve users anywhere, at anytime. However, taking into account the dynamic nature of user needs and usage situations, is a novel and non trivial undertaking. In essence, it is a fundamental change that requires designers to rethink many of the conventional answers and processes that help guide the creation of interactive systems. We provide a promising approach. / Master of Science

In this thesis we discuss the issue of searching the best k objects from the multi-users point of view. Every user has his own preferences, which are represented by fuzzy functions and aggregation function. This thesis designs and implements several solutions of searching the best k objects when attributes data are stored on remote servers. It was necessary to modificate existing algorithms for this type of obtaining data. This thesis uses several variants of Fagin algorithm, indexing methods using B+ trees and communication via web services.