The increased interest for self-tracking through the use of technology has given birth to the Quantified Self movement. The movement empowers users to gain self-knowledge from their own data. The overall idea is fairly recent and as such it provides a vast space for exploration and research. This project contributes to the Quantified self movement by proposing a concept for visualization of personal data using an avatar. The overall work finds inspiration in Chernoff faces visualization and it uses parts of the presentation method within the project design.   This thesis presents a visualization approach for Quantified Self data using avatars. It tests the proposed concept through a user study with two iterations. The manuscript holds a detailed overview of the designing process, questionnaire for the data mapping, implementation of the avatars, two user studies and the analysis of the results. The avatars are evaluated using Spotify data. The implementation offers a visualization library that can be reused outside of the scope of this thesis. The project managed to deliver an avatar that presents personal data through the use of facial expressions. The results show that the users can understand the proposed mapping of data. Some of the users were not able to gain meaningful insights from the overall use of the avatar, but the study gives directions for further improvements of the concept. / Visualizing quantified self data using avatars

New digital distribution and value capture models have emerged on the global music industry scene in the last ten years. These models are highly dependent on a strong underlying communications and broadband internet infrastructure which is largely present in most developed markets. South Africa, however, is a developing country whose broadband infrastructure is still nascent and not as widely available or accessible. Due to the permeation of mobile technology, most broadband internet access is likely to occur through the mobile networks in most developing markets. This stands in stark contrast to developed markets where broadband internet access occurs primarily via fixed line infrastructure with mobile as a secondary option.This research set out to investigate whether digital value distribution and value capture models which succeeded in developed countries would be suitable for the South African music industry given the broadband infrastructure constraints and the different internet access methodologies prevalent in this developing market. This research employed an exploratory research methodology in order to investigate this question and found that a mixture of value distribution and value capture models would address the entire market requirement, with mobile-centric digital distribution models being most suitable for mass market deployment. / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

Mobile media OTT service such as mobile music and mobile video is believed to be the next big revenue driver for mobile operator’s mobile broadband business. The media industry is willing to take mobile distribution channel to increase the content value. In Europe, there are already several successful cooperation cases between online media service provider and mobile operator, such as Spotify and Telia. However, with more complicated market and business environment in China, the mobile media services are still under development with immature business models.This thesis report aims to identify the business models of mobile media services in Europe and China, and to give business solution and strategic recommendation after analysis on actors, business roles, resources and activities using several business model theories. Six interviews have been done with industrial people from mobile network operator, online media service provider and TV channel in Sweden and China. With Spotify and Telia cooperation as the major part of case study in Europe market, data was gathered through business intelligent reports and interviews in Sweden. Six elements of business model theory from Chesbrough & Rosenbloom are used to conduct business model analysis onTelia-Spotify case. The result shows that Spotify play a joint connection role between tons of content from music insustry and mobile connection provider Telia, delivering value of good user experience and brand. Telia put the value of network, billing capability and user resource in the partnership with Spotify and benefit from the differentiated mobile broadband service, in terms of incearsing customer experience and reducing churn rate. TV4 case is investigated to analyze thebusiness development of mobile video in Europe. The result shows that there are two obstacles when delivering TV content to mobile device from TV channel’s perspective. One is that there is no way to measure the advertisement rate on mobile device, the other is that it is difficult to do localized advertisement program on mobile network. In the business model analysis in China, actors from mobile operator, state-owned media group and Internet company are identified as well as their activities in mobile media service. ARA model theory is used to analyze the value network and give the clear map of different actors’relations. The result shows that mobile media service in China has different situation in platform development, partnership with labels, service provider organization property and revenue model. Chinese mobile operators as state-owned companies develop mobile media service platform ontheir own instead of cooperation with service provider to avoid political and finance risk. They have direct partnership with content provider and give 50% of media OTT service revenue to content provider. The pirate rate of music in China is high, which result in very little willingness to pay for the music streaming service. Internet companies can build freemium service model with both licensed music content and content from illegal aggregators on advertising revenue model. However in online video area, Chinese government have implement strict regulatory to protect the copyright of video content. The major part of online video service providers’ revenueis also advertising, but with only licensed video content. The main factor that makes the different business environment between Europe and China is that the state-owned economy force in China plays important role in mobile media area. State owned mobile operators take the most business roles in mobile media service, which makes the possibility to cooperate with Spotify-like Internet companies is low. The first patch 3G mobile licenses are only granted to eight state-owned media groups, which are the only possible content providers of mobile operators’ mobile video service. To address the problem of cooperation inbetween mobile operators and Internet companies, to setup joint venture is the recommended solution. High customer satisfied service platform should be developed to enlarge the value of service. The recognized service value will lower the piracy rate and make the subscription revenue model possible.

Sonification allows for the analysis and interpretation of data through its rendering into sound. It is a method that remains largely unexplored from a digital humanities perspective. The present thesis addresses this research gap by exploring aspects of sonification as a mode of data perceptualization. Methodologically, it combines an extensive review of sonification literature with the design of a sonification prototype. The prototype is created using an exploratory programming approach and is evaluated through a small user study. One of the main challenges of using sonification within the humanities consists in aligning it with humanistic interpretative and critical modes of inquiry, in the thesis understood through the framework of Johanna Drucker’s critical hermeneutics. Another challenge is related to the shortage of existing projects and previous research, and also to a lack of relatively easy-to-use sonification tools. In the thesis, these challenges are primarily made visible through the process of prototype design and through the subsequent user study. Nevertheless, the literature review successfully uncovers a number of potential strategies for the realization of humanistic-interpretative sonification design, focusing especially on the concepts of phenomenology of listening and musical sonification. By applying musical sonification, it becomes possible to move beyond the simple representation of data, allowing instead for interpretative approaches. The findings presented in the thesis provide foundations for future research into humanities sonification.

Music listening is considered one of the most ubiquitous activities in everyday life, and one of the main reasons why people listen is to affect and regulate their mood. The vast availability and unlimited access of music has made it difficult to find relevant music that fits both the context and the preferences of the music listener. The aim of this project was to investigate the personalized relationship between music and mood using everyday technologies, focusing on how a listening experience could be adapted to the desired affect of a music listener while also taking the user’s individual listening history into account. In large, the project concentrated on the possibility of using context-aware music recommendation to generate personalized playlists by focusing on the audio features and corresponding mood of the music. A web-based application was developed to act as a prototype for the study, where the application allowed users to connect to Spotify, pick a desired mood and generate a playlist. By allowing people to access music in this personalized way, a user study could be conducted in order to investigate their music listening while incorporating this recommendation tool. The findings showed that the users’ found the experience to be engaging in that they could use the application as a companion to everyday tasks in addition to it being a tool for getting new, personalized music recommendations. Overall, the participants also found the generated playlists to be accurate to their music preferences and desired affective state.

Nowadays, smartphones are considered an essential part of our everyday lives. However, as the use of them increases so does their presence in vehicle environments. Prior studies have found that interacting with mobile devices while driving can cause cognitive, manual, and visual distractions. These distractions have a significant effect on driving performance and can result in serious accidents. As a solution to this, some applications, such as Spotify, has taken a different approach by developing a simplified version of their graphica linterface that is specifically designed for driving.The aim of this study is to examine what impacts simplified graphical interfaces has on the user experience for car drivers, focusing on Spotify and their car mode. In order to examine this, an online survey and a feature analysis was conducted. The result of this study shows that Spotify's car mode is missing a lot of features that are important to the users, causing them not to use it. These results highlight the challenge of providing all features that are expected by the users while creating a simpler interface.

A lot of today’s marketing have moved to social media-platforms, especially Facebook. But how do companies use this media to communicate to their customers? And what does the customers who follow the companies on Facebook feel about the content being published? The purpose of this study is to get a better understanding on what two large companies, H&M and Spotify, publish on Facebook and to find out what their followers perception of the content is. The methods used are mixed methods, meaning a mix between qualitative and quantitative methods and a comparative analysis on the result from both methods. The qualitative methods are in the form of interviews with ten people, five that follows H&M and five that follow Spotify on Facebook. A content analysis is performed on all the content published by H&M and Spotify during a six month period, from 01-02-16 to 30-07-15. The study shows that both companies primarily used relationship marketing on their Facebook-pages by publishing mostly recommendations and information from either the music- or fashion market rather than their own products, and that these follower’s thought the content was diverse and relevant to them as a target audience. / Sociala medie-kanaler så som Facebook inkluderas allt mer inom dagens marknadsföring. Men hur använder företag detta medium för att kommunicera med sina kunder? Och vad tycker kunderna som följer företag på Facebook om innehållet? Syftet med denna studie är att få en större förståelse om vad två stora företag, H&M och Spotify, publicerar på Facebook och ta reda på vad deras följares uppfattning är kring innehållet. De metoder vi har använt oss av är mixed methods, som innebär en blandning av kvalitativa och kvantitativa metoder och en jämförande analys av resultatet från de båda. Den kvalitativa metoden är i intervjuform med tio personer; fem som följer H&M och fem som följer Spotify. En innehållsanalys gjordes på alla inlägg som H&M och Spotify publicerade mellan 01-02-16 till 30-07-15. Resultatet av studien visade att de båda företagen främst arbetade med relationship marketing på deras Facebook-sidor genom att mest publicera rekommendationer och information om musik- och modebranschen, mer än deras egna produkter och tjänster. Intervjuerna visade att de undersökta följarna också fann detta innehåll varierat och relevant för dem som målgrupp

Denna studie syftar att undersöka huruvida animation som visuellt komplement på Spotify väcker större engagemang emotionellt än ett statiskt skivomslag. Animerade filmer har tagits fram till sex olika låtar på albumet 99.9% av artisten Kaytranada. Detta har gjorts genom en förstudie i form av en enkät, samt research om de befintliga filmer som finns tillgängliga på Spotify i dagsläget. Efter detta har designarbetet utförts genom brainstorming, explorativt skissande och storyboards. För att utvärdera skapades en värderingsmatris med syfte att välja ut de mest engagerande idéerna, för att sedan fortsätta med digitalt skissande och slutligen animering. Efter detta värderades animationerna gentemot det statiska skivomslaget genom en kvalitativ enkät, såväl som två olika fokusgrupper. Resultatet visade att även om animationerna ansågs något repetitiva så ansåg en majoritet att de var mer engagerande emotionellt än ett statiskt skivomslag.

In this thesis, various machine learning domains have been combined in order to build a video recommender system that is based on object detection. The work combines two extensively studied research fields, recommender systems and computer vision, that also are rapidly growing and popular techniques on commercial markets. To investigate the performance of the approach, three different content-based recommender systems have been implemented at Spotify, which are based on the following video features: object detections, titles and descriptions, and user preferences. These systems have then been evaluated and compared against each other together with their hybridized result. Two algorithms have been implemented, the prediction and the top-N algorithm, where the former is the more reliable source for evaluating the system's performance. The evaluation of the system shows that the overall performance scores for predicting values of the users' liked and disliked videos are in the range from about 40 % to 70 % for the prediction algorithm and from about 15 % to 70 % for the top-N algorithm. The approach based on object detection performs worse in comparison to the other approaches. Hence, there seems to be is a low correlation between the user preferences and the video contents in terms of object detection data. Therefore, this data is not very suitable for describing the content of videos and using it in the recommender system. However, the results of this study cannot be generalized to apply for other systems before the approach has been evaluated in other environments and for various data sets. Moreover, there are plenty of room for refinements and improvements to the system, as well as there are many interesting research areas for future work.

For many companies today, it is highly valuable to collect and analyse data in order to support decision making and functions of various sorts. However, this kind of data-driven approach is seldomly applied to software testing and there is often a lack of verification that the testing performed is relevant to how the system under test is used. Therefore, the aim of this thesis is to investigate the possibility of introducing a data-driven approach to test automation by extracting user behaviour data and curating it to form input for testing. A prestudy was initially conducted in order to collect and assess different data sources for augmenting the testing. After suitable data sources were identified, the required data, including data about user activity in the system, was extracted. This data was then processed and three prototypes where built on top of this data. The first prototype augments the model-based testing by automatically creating models of the most common user behaviour by utilising data mining algorithms. The second prototype tests the most frequent occurring client actions. The last prototype visualises which features of the system are not covered by automated regression testing. The data extracted and analysed in this thesis facilitates the understanding of the behaviour of the users in the system under test. The three prototypes implemented with this data as their foundation can be used to assist other testing methods by visualising test coverage and executing regression tests.