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81	Data-driven test automation : augmenting GUI testing in a web application Kurin, Erik, Melin, Adam January 2013 (has links) 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. software testing regression testing model-based testing test automation Graphwalker Hadoop MapReduce cluster analysis Markov chain K-means data mining Spotify test visualization user behaviour analysis Computer Engineering Datorteknik
82	Marketingová a komunikační strategie pro streamovací službu Spotify / Marketing and communication strategy of Spotify streaming service Petlach, Radim January 2016 (has links) The objective of this Masters`s Thesis is an analysis of marketing and communication strategy of Spotify, the music streaming service, and subsequent presentation of proposals and recommendation with the aim to improve existing strategy. The thesis consists of theoretical and practical part. In the theoretical part, basic terms such as marketing, marketing mix, marketing and communication strategy, are defined. In the practical part, streaming service Spotify, its marketing and communication mix and competitors analysis, are described. A substantial part of this thesis is own market research in a form of a survey. Findings and results derived from primary and secondary sources help the author to draw conclusions and provide recommendation for improvement of existing marketing and communication strategy of Spotify in the Czech Republic.
83	Cost optimization in the cloud : An analysis on how to apply an optimization framework to the procurement of cloud contracts at Spotify Ekholm, Harald, Englund, Daniel January 2020 (has links) In the modern era of IT, cloud computing is becoming the new standard. Companies have gone from owning their own data centers to procuring virtualized computational resources as a service. This technology opens up for elasticity and cost savings. Computational resources have gone from being a capital expenditure to an operational expenditure. Vendors, such as Google, Amazon, and Microsoft, offer these services globally with different provisioning alternatives. In this thesis, we focus on providing a cost optimization algorithm for Spotify on the Google Cloud Platform. To achieve this we construct an algorithm that breaks up the problem in four different parts. Firstly, we generate trajectories of monthly active users. Secondly, we split these trajectories up in regions and redistribute monthly active users to better describe the actual Google Cloud Platform footprint. Thirdly we calculate usage per monthly active users quotas from a representative week of usage and use these to translate the redistributed monthly active users trajectories to usage. Lastly, we apply an optimization algorithm to these trajectories and obtain an objective value. These results are then evaluated using statistical methods to determine the reliability. The final model solves the problem to optimality and provides statistically reliable results. As a consequence, we can give recommendations to Spotify on how to minimize their cloud cost, while considering the uncertainty in demand. cloud computing Spotify monte carlo MC stochastic processes stochastic programming Google Cloud Platform GCP IT computational resources optimization cost optimization forecast procurement Mathematics Matematik Probability Theory and Statistics Sannolikhetsteori och statistik Mathematical Analysis Matematisk analys Computational Mathematics Beräkningsmatematik
84	What's in a name? How the vocabulary of personalised playlists affects user's expectation and satisfactions in music streaming services Boksjö, Nina, Petricioiu, Naomi January 2022 (has links) Background: The following study focuses on the area of personalisation within streaming services and how vocabulary of playlist names and categories affect expectations and satisfactions. The wording of personalised items is important to convey that content is directly made for a user, yet there are limited studies that explore what users anticipate and if the message conveys correct information to then lead to satisfaction. Purpose: By using Spotify as the prime focus, this research aims to uncover how the vocabulary used in the categories of playlists and playlist titles impacts the user’s expectations and satisfaction with the actual playlist content. Method: The study uses a qualitative approach and semi-structured interviews as data collection. The interviews proceeded with open-ended questions to be able to gain a deeper understanding of the participants opinions and experiences. The analysis of the data is interpreted deductively through a thematic analysis which allowed for common topics, ideas and repeated meanings to be conveyed. Personalisation Recommender Systems Vocabulary Spotify Personalised Messages Streaming Services User Interface Expectation Satisfaction User Experience Human Computer Interaction Information Systems, Social aspects
85	Keep it Local: Music Streaming & Local Music Communities Jones, Richard Earl 01 December 2017 (has links) No description available. Music Mass Communications Mass Media Technology Performing Arts Multimedia Communications Fine Arts music streaming spotify apple recording artists live performance music streaming napster pandora iTunes iPod YouTube Netflix RIAA design soundcloud bandcamp record labels experience design music industry revenue radio college radio

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