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No. of bitstreams: 1 33.pdf: 1765508 bytes, checksum: d921fbdef8015531446e414c52c66bf9 (MD5) Previous issue date: 2012 / Nenhuma / Este trabalho elabora de uma arquitetura de software que contempla dentro de dispositivos móveis na plataforma Android, a coleta de informações de contexto físico de localização (informações que são apenas coletadas em ambientes externos) e geração de contexto lógico de localização (informações que precisam de um processamento dos dados para ser encontradas em ambientes internos), estas informações são armazenadas em uma estrutura Web Semântica a qual sofrerá inferências para gerar mais um contexto lógico de recomendação de uso de recursos disponíveis no dispositivo móvel e anteriormente utilizados pelo usuário em um dado instante e local. A funcionalidade desta arquitetura será verificada com a construção de um protótipo na plataforma Android. Um dos desafios deste trabalho será coletar o contexto lógico de localização do dispositivo em locais internos, como prédios e casas, onde a intensidade do sinal do sistema de posicionamento global (GPS) é insuficiente para ser identificada, portanto neste trabalho será utilizado sensores acelerômetro e giroscópio presentes nos dispositivos móveis para calcular seu deslocamento. A localização interna será integrada a localização externa, formando um percurso contínuo. As informações coletadas no contexto físico são armazenadas em uma ontologia dentro do dispositivo móvel e sincronizadas com um servidor remoto. Outro desafio deste trabalho é o desenvolvimento de um agente de software que através dos dados armazenados na ontologia local, faz inferências nos dados armazenados na forma de Web Semântica e disponibiliza recomendações de uso de um determinado recurso, fundamentado apenas nos dados históricos de utilização destes recursos, relacionando a aproximação em determinado local com a frequência no tempo em relação ao mesmo horário do dia ou ao mesmo dia da semana e ao mesmo dia do mês. O armazenamento do contexto coletado, em uma estrutura Web Semântica, possibilita a união destas informações com demais informações coletadas de outros dispositivos contendo contextos que caracterizem um equipamento, um indivíduo ou uma sociedade. O resultado esperado da arquitetura apresentada neste trabalho, será o maior grau possível de precisão na posição geográfica identificada e a coerência das recomendações de uso de recursos disponíveis no dispositivo móvel em um dado instante e local. / This paper elaborates a software architecture that addresses within mobile devices on the Android platform, collecting information from the physical context of location (only information that is collected outdoors) and generation of logical context of location (information they need processing of the data to be found indoors) and stores this information in a Semantic Web structure which suffer inferences to generate a context logical of recommendation to use resources available on the mobile device and used previously by the user at a given time and local. The functionality of this architecture will be test by construction a prototype on the Android platform. One of the challenges of this work will be to collect the context of logical device location in indoor locations such as buildings and houses where the signal strength of the Global Positioning System (GPS) is insufficient to be identified, so this work will be used and accelerometer sensors gyroscope present in mobile devices to calculate your speed and direction. The location will be integrated inside the external location, forming a continuous path. The information collected in the physical context is stored in the ontology within the mobile device and synchronized with a remote server. Another challenge of this work is the development of a software agent that through data stored in the ontology on device, makes inferences on the data stored in the form of Web Semantic and provides recommendations for use of a given resource, based only on historical data of these resources by relating the approach in a certain place with the frequency in time over the same time of day or the same day of the week and the same day of the month. The architecture of this work is being called and Context Manager is integrated with the other two studies did not present this work: a Semantic Desktop with the task of identifying a resource that is being used to send and manager context; and Context's Federation, serving as a remote server, with the task of receiving context data collected by the context manager. The storage of context collected in a Web Semantic structure enables the union of this information with other context information that characterize a device, an individual or a society. The expected outcome of the architecture presented here will be the greatest possible degree of accuracy in the identified geographical position and consistency of recommendations for the use of resources available on the mobile device at a given time and place.

L'évolution rapide du web a changé la façon dont l'information est créée, distribuée, évaluée et consommée. L'utilisateur est dorénavant mis au centre du web en devenant le générateur de contenu le plus prolifique. Pour évoluer dans le flot d'informations, les utilisateurs ont besoin de filtrer le contenu en fonction de leurs centres d'intérêts. Pour bénéficier de contenus personnalisés, les utilisateurs font appel aux réseaux sociaux ou aux systèmes de recommandations exploitant leurs informations privées. Cependant, ces systèmes posent des problèmes de passage à l'échelle, ne prennent pas en compte la nature dynamique de l'information et soulèvent de multiples questions d'un point de vue de la vie privée. Dans cette thèse, nous exploitons les architectures pair-à-pair pour implémenter des systèmes de recommandations pour la dissémination personnalisée des news. Une approche pair-à-pair permet un passage à l'échelle naturel et évite qu'une entité centrale contrôle tous les profils des utilisateurs. Cependant, l'absence de connaissance globale fait appel à des schémas de filtrage collaboratif qui doivent palier les informations partielles et dynamiques des utilisateurs. De plus, ce schéma de filtrage doit pouvoir respecter la vie privée des utilisateurs. La première contribution de cette thèse démontre la faisabilité d'un système de recommandation de news totalement distribué. Le système proposé maintient dynamiquement un réseau social implicit pour chaque utilisateur basé sur les opinions qu'il exprime à propos des news reçues. Les news sont disséminées au travers d'un protocole épidémique hétérogène qui (1) biaise l'orientation des cibles et (2) amplifie la dissémination de chaque news en fonction du niveau d'intérêt qu'elle suscite. Ensuite, pour améliorer la vie privée des utilisateurs, nous proposons des mécanismes d'offuscation permettant de cacher le profil exact des utilisateurs sans trop dégrader la qualité de la recommandation fournie. Enfin, nous explorons un nouveau modèle tirant parti des avantages des systèmes distribués tout en conservant une architecture centralisée. Cette solution hybride et générique permet de démocratiser les systèmes de recommandations en offrant aux fournisseurs de contenu un système de personnalisation à faible coût. / The rapid evolution of the web has changed the way information is created, distributed, evaluated and consumed. Users are now at the center of the web and becoming the most prolific content generators. To effectively navigate through the stream of available news, users require tools to efficiently filter the content according to their interests. To receive personalized content, users exploit social networks and recommendation systems using their private data. However, these systems face scalability issues, have difficulties in coping with interest dynamics, and raise a multitude of privacy challenges. In this thesis, we exploit peer-to-peer networks to propose a recommendation system to disseminate news in a personalized manner. Peer-to-peer approaches provide highly-scalable systems and are an interesting alternative to Big brother type companies. However, the absence of any global knowledge calls for collaborative filtering schemes that can cope with partial and dynamic interest profiles. Furthermore, the collaborative filtering schemes must not hurt the privacy of users. The first contribution of this thesis conveys the feasibility of a fully decentralized news recommender. The proposed system constructs an implicit social network based on user profiles that express the opinions of users about the news items they receive. News items are disseminated through a heterogeneous gossip protocol that (1) biases the orientation of the dissemination, and (2) amplifies dissemination based on the level of interest in each news item. Then, we propose obfuscation mechanisms to preserve privacy without sacrificing the quality of the recommendation. Finally, we explore a novel scheme leveraging the power of the distribution in a centralized architecture. This hybrid and generic scheme democratizes personalized systems by providing an online, cost-effective and scalable architecture for content providers at a minimal investment cost.

近來，旅遊資訊廣被旅遊者在網路上使用。雖然網路上的資訊十分豐富，但是使用者仍常常難以找尋到精準的資訊。而旅遊商品的特性為無形的，所以使用者不能實際地來評估這個服務直到他實際地體驗之後。也就是因為此種特性，所以如何讓使用者在真正體驗到之前能夠取得可信與真實的旅遊資訊變得十分重要。為了解決此問題，語意網絡的概念即出現來解決人與電腦間溝通的問題。而一個本體即是由一個正式化的、某一具有精確規格概念的領域來提供之可實行的平台來發展可信的旅遊資訊服務。 在本論文中，我們探討了旅遊推薦系統的發展、其遭遇的問題、語意網相關之技術包含了：網路本體語言、資源描述架構、和一些目前現有的旅遊本體發展的情況。此外，為了要能提供更智慧化的旅遊行程規劃推薦服務，我們將語意的想法帶入了此領域。我們會提出一個方法讓智慧型旅遊行程推薦服務能在本體論的基礎上實現。所以，一系列的旅遊本體會被建構發展，來讓我們的芻形系統能夠做出行程推薦的服務。此提出的系統能夠驗證語意網的概念在旅遊推薦領域的可行性。它亦能利用屬性與之間的關係來推薦出更智慧型的資訊，找出個人化的景點、活動與行程給旅行者。 / Nowadays, travel information is increasing to appeal the tourists on the web. Al- though there are numerous information provided on the web, the user gets puzzled in nding accurate information. The tourism product has an intangible nature in that cus- tomers cannot physically evaluate the services on oer until practically experienced. This makes access to credible and authentic information about tourism products before the actual experience very valuable. In order to solve these problems, the concept of seman- tic web comes into existence to have communication between human and computer. An Ontology being a formal, explicit specication of concepts of a domain provides a viable platform for the development of credible tourism information services. In this paper, we discuss the development of travel recommendation system, the problems it encounters, the related technology about semantic web including OWL, RD- F/RDFS, and some current circumstances of the existing tourism ontologies as well. Futhermore, in order to make more intelligent travel planning recommendation services, we bring the idea of semantic into tourism domain. We will present an approach aimed at enabling intelligent recommendation services in tourism support systems using ontolo- gies. A suite of tourism ontologies was developed and engaged to enable a prototypical tourism system with recommednation capabilities. The proposed system can verify the feasibility and concept of taking semantic web technology into tourism recommendation systems domain. It also can recommend more intelligent information using properties, relationships of travel ontology, and is responsible for nding personalized attractions, activities and a trip itinerary for travelers.

The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquo / Which one should I choose?&rdquo / arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.

推薦系統目的在於收集大眾對於資源的評價，經過推薦機制的整理，呈現給符合偏好的使用者，但是Supernode架構與傳統主從架構的不同，增加推薦系統收集、統計推薦指標的困難。在本篇論文中，將利用RDF(S)建置與Supernode、Peer、MP3相關的本體論，同時，依據Supernode與Peer在推薦系統中所扮演的功能與角色的不同，將不同的結構藍圖分別放置在Supernode與Peer之中，並且藉由Supernode與Peer中所描述的資源、屬性以及資源和資源之間的關係來解決在Supernode架構中推薦指標的收集、統計與更新等問題，以及降低推薦系統中"Cold Start"與"Spamming"這兩個問題所產生的影響。 / The purpose of recommendation system is to collect public opinions and provide synthetic recommended result for users. Using our proposed recommended mechanism, the recommendation system presents personal music recommended list for each user in peer node to match his preference. Because the complexity difference of network structure, we found that the rating collection and computation process will be more complex in supernode P2P network than in client-server WWW network. In this thesis, firstly we use RDF(S) to construct associated ontology schema for supernode, peer node, and MP3 music. Then, we put respective ontology schema in the suprenode and peer node based on their roles and functions within the recommendation system. The resources and attributes relationships between supernode and peer nodes provide the capacity to solve the rating collection, analysis, and updating problems. We also propose possible solutions for reducing the side effects of cold start and spamming problems in the recommendation system.

在西洋棋中，開局決定了棋局未來發展的基礎，棋手在開局階段局勢的好壞，會直接影響到接下來中局的發展，乃至全局的勝負。隨著西洋棋的演進，棋手們在比賽中進行各式各樣的棋步嘗試，發展出眾多經歷實戰考驗的開局，目前西洋棋的開局多達上千種變化，使得棋手在學習西洋棋的過程中，要花上大量的時間從眾多的西洋棋開局變化中，尋找適合自己的開局鑽研與使用。為幫助棋手在此階段的學習，本論文提出西洋棋開局推薦系統，從大數據協助學習的觀點，利用大量棋手們的開局經驗，對棋手做個人化的開局推薦。此系統以風格、棋力相似的棋手們所選用的開局為推薦基礎，並考量棋手習慣使用的下棋模式，推薦棋手善於發揮自身優勢、易於理解，並且投其所好的開局。為此，此西洋棋開局推薦系統包含風格分析、棋力評估、棋形截取，以及混合式推薦等部分。依據棋手過去的對局記錄，風格分析評估棋手下棋偏好冒險或保守的程度；棋力評估將傳統西洋棋棋力轉成可直觀比較棋手棋力程度差異之量表；棋形截取找出棋手習慣使用的下棋模式。最後，混合式推薦綜合考量上述三項因素，推薦出符合棋手棋風、棋力與下棋習慣模式的開局。 本論文以兩個實驗來評估風格分析與開局推薦系統的效果，在風格分析的實驗中，將風格分析方法評估棋手風格的結果與專家判斷的結果做比較；在開局推薦系統的實驗中，以棋手是否將會在比賽使用系統所推薦的開局來評估推薦效果。實驗結果顯示，風格分析對於世界冠軍棋手的風格評估幾乎與專家的判斷相同；開局推薦系統針對開局所設計的混合式推薦方法，推薦效果優於常見的推薦方法。 / The Opening is the fundamental phase of a chess game, and significantly affects the result of a competition. With the evolution of chess, there has been developed thousands of chess openings at present. This makes it difficult and time-consuming for chess players to find and learn the openings suitable for them. For helping players to learn chess in the opening, we provide Opening Recommendation System (OPRS), which considers chess players’ experiences and recommends chess openings that could be understandable and favorite for the players. For personalized recommendation, OPRS analyzes the playing style, translates chess rating, extracts the playing patterns, and then performs hybrid recommendation based on the features obtained. In the evaluation, the performance of the playing style analysis are demonstrated by comparing with the styles judged by chess experts for world chess championships. For OPRS, the evaluations are according to the openings the players use in the chess tournaments in the next years. The experiments show that OPRS achieves good accuracies of the playing style analysis and outperforms the competitive methods for chess opening recommendation.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The management of problems occurred in environments that make use of Information Technology (IT), coupled with the need for a rapid response support teams, makes organizations require systems to manage these incidents. The Service Desk presents itself as a good solution to centralize these records. Concepts based computing context, recommender systems, mobile computing can enhance these applications. Therefore, the aim of this work is to design and implement a system of Service Desk Mobile, called Mobile Service Desk (MSD), which adds context awareness features such as user location, technical experience and temporal context. Moreover, the tool is integrated into a recommendation system, which stores past interactions and suggests as a possible solution for new similar incidents occurring in the managed environment. As contributions of this work, in addition to system design that aims to reduce unnecessary time-shift and optimize the allocation of technical, algorithms were compared for similarity analysis and applied to NBR 9241-11 for usability evaluation of some products Service Desk. / A gerência dos problemas ocorridos em ambientes que fazem uso da Tecnologia da Informação (TI), aliada a necessidade de uma resposta rápida das equipes de suporte, faz com que organizações necessitem de sistemas para gerenciamento desses incidentes. O Service Desk apresenta-se como uma boa solução para centralizar estes registros. Conceitos de computação baseada em contexto, sistemas de recomendação, computação móvel podem incrementar estes aplicativos. Portanto, o objetivo deste trabalho é projetar e implementar um sistema de Service Desk móvel, denominado Mobile Service Desk (MSD), que agrega funcionalidades de sensibilidade ao contexto, tais como localização do usuário, experiência do técnico e contexto temporal. Além disso, está integrado à ferramenta um sistema de recomendação, que armazena interações passadas e as sugere como possível solução para novos incidentes similares ocorridos no ambiente gerenciado. Como contribuições deste trabalho, além do projeto do sistema que visa redução de tempo com deslocamentos desnecessários e otimização do alocamento de técnicos, foram comparados algoritmos para análise de similaridade e aplicado a norma NBR 9241-11 para avaliação de usabilidade de alguns produtos de Service Desk.

Este artículo trata sobre la problemática y el desarrollo de un asistente virtual de moda propuesto mediante el uso de una cámara Kinect v2 y procesamiento de imágenes, para tiendas minoristas de moda. Surge principalmente como una respuesta a la incapacidad de proporcionar experiencias únicas durante el proceso de compra mediante el uso de diversos dispositivos. Debido a esto, se analizaron soluciones de asistente virtual similares orientadas a proporcionar recomendaciones de vestimenta para poder proporcionar un software que podría dar una sugerencia más personalizada para los usuarios basándose en sus características físicas. La validación del modelo se realizará a través de la experiencia de un grupo de usuarios en el momento del uso del asistente virtual y expertos en el área de evaluación de moda. / This article is about the problematic and development of a fashion virtual assistant proposed by using a Kinect v2 camera and image processing, for fashion retail stores. It comes up mainly as a response to the inability of providing unique experiences during the shopping process through the use of diverse devices. Because of this, similar virtual assistant solutions oriented to provide clothing recommendations were analysed to be able to provide a software that could give a more personalized suggestion for the users basing in their physical characteristics. The model validation will be done through the experience of a group of users at the moment of use of the virtual assistant and experts in the fashion assessment area. / Trabajo de investigación

En classification multi-labels graduée (CMLG), chaque instance est associée à un ensemble de labels avec des degrés d’association gradués. Par exemple, une même molécule odorante peut être associée à une odeur forte ‘musquée’, une odeur modérée ‘animale’, et une odeur faible ‘herbacée’. L’objectif est d’apprendre un modèle permettant de prédire l’ensemble gradué de labels associé à une instance à partir de ses variables descriptives. Par exemple, prédire l’ensemble gradué d’odeurs à partir de la masse moléculaire, du nombre de liaisons doubles, et de la structure de la molécule. Un autre domaine intéressant de la CMLG est les systèmes de recommandation. En effet, les appréciations des utilisateurs par rapport à des items (produits, services, livres, films, etc) sont d’abord collectées sous forme de données MLG (l’échelle d’une à cinq étoiles est souvent utilisée). Ces données sont ensuite exploitées pour recommander à chaque utilisateur des items qui ont le plus de chance de l’intéresser. Dans cette thèse, une étude théorique approfondie de la CMLG permet de ressortir les limites des approches existantes, et d’assoir un ensemble de nouvelles approches apportant des améliorations évaluées expérimentalement sur des données réelles. Le cœur des nouvelles approches proposées est l’exploitation des relations entre les labels. Par exemple, une molécule ayant une forte odeur ‘musquée’ émet souvent une odeur faible ou modérée ‘animale’. Cette thèse propose également de nouvelles approches adaptées au cas des molécules odorantes et au cas des gros volumes de données collectées dans le cadre des systèmes de recommandation. / In graded multi-label classification (GMLC), each instance is associated to a set of labels with graded membership degrees. For example, the same odorous molecule may be associated to a strong 'musky' odor, a moderate 'animal' odor, and a weak 'grassy' odor. The goal is to learn a model to predict the graded set of labels associated to an instance from its descriptive variables. For example, predict the graduated set of odors from the molecular weight, the number of double bonds, and the structure of the molecule. Another interesting area of the GMLC is recommendation systems. In fact, users' assessments of items (products, services, books, films, etc.) are first collected in the form of GML data (using the one-to-five star rating). These data are then used to recommend to each user items that are most likely to interest him. In this thesis, an in-depth theoretical study of the GMLC allows to highlight the limits of existing approaches, and to introduce a set of new approaches bringing improvements evaluated experimentally on real data. The main point of the new proposed approaches is the exploitation of relations between labels. For example, a molecule with a strong 'musky' odor often has a weak or moderate 'animal' odor. This thesis also proposes new approaches adapted to the case of odorous molecules and to the case of large volumes of data collected in the context of recommendation systems.

The industry of electronic commerce (e-commerce) constitutes a great part of the yearly retail consumption in Sweden. Looking at recent years, it has been seen that a rapidly growing sector within the mentioned field is the clothing industry for clothes and accessories for children and newborns. To get an overview of the items and help customers to find what they are looking for, many web stores have a system called a Recommendation System. The mechanics behind this service can look rather different depending on the method used. However, their unified goal is to provide a list of recommended items of interest to the customer.  A branch within this field is the Session Based Recommendation System (SBRS). These are models which are designed to work with the trace of products, called a session, that a user currently has visited on the web store. Based on that information they then formulate an output of recommended items. The SBRS models have been especially popularized since the majority of customers browse in an anonymous behavior, which means that they due to time efficiency often neglect the possibility of creating or logging into any personal web store account. This however limits the accessible information that a system can make use of to shape its item list.  It can be seen that the number of articles exploring SBRS within the fashion branch of clothing and accessories for children is very limited. This thesis is made to fill that gap. After a thorough literature study, three models were found to be of certain interest, the Short-Term Attention/Memory Priority (STAMP) model, Long Short-Term Memory (LSTM) model, and Gated Recurrent Unit (GRU) model. Further, the LSTM model is included as it is the collaborative company, BabyShop Group AB's current used method.  The results of this thesis show that the GRU model is a promising method, managing to predict the next item for a customer more consistently than any other of the evaluated models. Furthermore, it can also be seen that what embeddings the models use to represent the products plays a significant role in the learning and evaluation of the used data set.  Moreover, a benchmark model included in this thesis also shows the importance of filtering the data set of sessions. It can be seen that a majority of customers visit already-seen products, logged happenings most likely due to refreshing web pages or similar actions. This causes the session data set to be characterized by repeated items. For future work, it would therefore indeed be interesting to see how this data set can be filtered in a different way. To see how that affects the outcome of the used metrics in this thesis. / Industrin för elektronisk handel (e-handel) utgör en stor del av den årliga konsumtionen av återförsäljning i Sverige. Bara genom att följa de senaste åren har det kunnat ses att en snabbt växande sektor inom det nämnda området är den som berör kläder och accessoarer för barn.  För att kunna ge en överblick och hjälpa kunder att finna vad de söker använder många webbutiker ett system som kallas rekommendationssystem. Hur dessa system faktiskt fungerar kan se väldigt olika ut. Men deras gemensamma mål är att i slutändan kunna ge en lista av rekommenderade produkter till kunden. En gren inom detta område är sessionsbaserade rekommendationssystem. Detta är modeller som är designade för att arbeta med själva spåret av besökta produkter, de som en kund har varit inne på under sin nuvarande vistelse på webbutiken. Baserat på denna information formuleras sedan en lista av rekommenderade produkter till besökaren. Dessa typer av modeller har blivit särskilt populära då många kunder gillar att shoppa anonymt. Vilket i denna kontext betyder att de gärna slipper att behöva logga in på något personligt konto på webbutiken, där särskild information kan sparas. Men detta betyder också att mängden tillgängliga data minskas för rekommendationssystemet.  Antalet forskningsartiklar som utforskar sessionsbaserade rekommendationssystem för e-handel inom barnmode är väldigt begränsad. Denna avhandling är därför gjord med syftet att försöka fylla detta tomrum. En genomgående litteraturstudie visade att tre modeller var av särskilt intresse, nämligen Short-Term Attention/Memory Priority (STAMP), Gated Recurrent Unit (GRU) och Long Short Term Memory (LSTM) modellen. Den sistnämnda är inkluderad då detta är den nuvarande modellen som används av företaget som denna avhandling har gjorts i samarbete med, BabyShop Group AB.  Resultaten i denna avhandling kan påvisa att GRU är en mycket lovande modell som lyckades förutbestämma nästkommande produkt i en sessionskedja bäst. Utöver detta kan det också ses att embedding-vektorerna som används för att representera produkterna för modellerna spelar en avgörande roll. Speciellt för deras lärande och evaluering av data.  Förutom det påvisade en av riktvärdesmodellerna som användes i denna avhandling den viktiga innebörden av att filtrera sessionsdata. Det kan nämligen urskiljas i den data som erhölls från företaget att många kunder återbesöker en stor del av redan besökta produkter. Detta åstadkommas troligen av att kunderna uppdaterar sidan de är på, eller utför någon annan liknande handling. Det här gör att en stor del av den sessionsdata som används i denna avhandling innehåller många upprepade produkter i de givna sessionskedjorna. Som framtida arbete vore det därför intressant att utforska olika filtreringsmetoder som kan appliceras på den givna datamängden. Detta för att se hur en mera filtrerad datamängd påverkar slutresultatet av de använda mätmetoderna i denna avhandling.