Browsing by master's degree program "Master's Programme in Computer Science"

Message-oriented middleware (MOM) serves as the intermediary component between the nodes of a distributed system, facilitating their communication and data exchange. By decoupling the interconnected nodes of a system, MOM technologies enable scalable and fault-tolerant messaging, supporting real-time data streams, event-driven architectures and microservices communication. Given the increasing reliance on distributed computing and data-intensive applications, understanding the performance and operational characteristics of MOM technologies is paramount. This master's thesis investigates the comparative performance and operational aspects of two prominent MOM solutions, Apache Kafka and Apache Pulsar, through a systematic literature review (SLR). The key characteristics under inspection are throughput, latency, resource utilization, fault tolerance, security and operational complexity. This study offers a comprehensive analysis to aid informed decision-making in real-world deployment scenarios and augments the existing body of literature. The results of this SLR show that consensus on throughput and latency superiority between Kafka and Pulsar remains elusive. Pulsar demonstrates advantages in resource utilization and security, whereas Kafka stands out for its maturity and operational simplicity.

In recent years, the concept of Metaverse has become a popular buzzword in the media and different communities. In 2021, the company behind Facebook rebranded itself into Meta Platforms, inc. in order to match their new vision of developing the Metaverse. The Metaverse is becoming reality as intersecting technologies, including head-mounted virtual reality displays (HMDs) and non-fungible tokens (NFTs), have been developed. Different communities, such as media, researchers, consumers and companies have different perspectives on the Metaverse and its opportunities and problems. Metaverse technology has been researched thoroughly, while little to none research has been done on gray literature, i.e. non-scientific sources, to gain insight on the ongoing hype. The conducted research analyzed 44 sources in total, ranging from news articles to videos and forum discussions. The results show that people are seeing opportunities in Metaverse entrepreneurship in the changing career landscape. However, the visions of Meta Platforms, inc. also receive a fair amount of critique in the analyzed articles and threads. The results suggest that most of the consumers are only interested in a smaller subset of features than what is being marketed. The conducted research gives insight on how different sources are seeing the Metaverse and can therefore be used as a starting point for more comprehensive gray literature studies on the Metaverse. While making innovations to the underlying technology is important, studying people’s viewpoints is a requirement for the academia to understand the phenomenon and for the industry to produce a compelling product.

Context: The Bank of Finland, as the national monetary and central bank of Finland, possesses an extensive repository of data that fulfills both the statistical needs of international organizations and the federal requirements. Data scientists within the bank are increasingly interested in investing in machine learning (ML) capabilities to develop predictive models. MLOps offers a set of practices that ensure the reliable and efficient maintenance and deployment of ML models. Objective: In this thesis, we focus on addressing how to implement an ML pipeline within an existing environment. The case study is explorative in nature, with the primary objective of gaining deeper insight into MLOps tools and their practical implementation within the organization. Method: We apply the design science research methodology to divide design and development into six tasks: problem identification, objective definition, design and development, demonstration, evaluation, and communication. Results: We select the tools for the MLOps based on the user requirements and the existing environment, and then we design and develop a simplified end-to-end ML pipeline utilizing the chosen tools. Lastly, we conduct an evaluation to measure the alignment between the selected tools and the initial user requirements.

The continuously evolving cyber threat landscape has become a major concern because sophisticated attacks against systems connected to the Internet have become frequent. The concern is on particular threats that are known as Advanced Persistent Threats (APT). The thesis aims to introduce what APTs are and illustrate other topics under the scope, such as tools and methods attackers can use. Attack models will also be explained, providing example models proposed in the literature. The thesis also introduces which kind of operational objectives attacks can have, and for each objective, one example attack is given that characterizes the objective. In addition, the thesis also uncovers various countermeasures, including most essential security solutions, complemented with more advanced methods. The last countermeasure that the thesis introduces is attribution analysis.

This thesis presents a wavelet-based method for detecting moments of fast change in the textual contents of historical newspapers. The method works by generating time series of the relative frequencies of different words in the newspaper contents over time, and calculating their wavelet transforms. Wavelet transform is essentially a group of transformations describing the changes happening in the original time series at different time scales, and can therefore be used to pinpoint moments of fast change in the data. The produced wavelet transforms are then used to detect fast changes in word frequencies by examining products of multiple scales of the transform. The properties of the wavelet transform and the related multi-scale product are evaluated in relation to detecting various kinds of steps and spikes in different noise environments. The suitability of the method for analysing historical newspaper archives is examined using an example corpus consisting of 487 issues of Uusi Suometar from 1869–1918 and 250 issues of Wiipuri from 1893–1918. Two problematic features in the newspaper data, noise caused by OCR (optical character recognition) errors and uneven temporal distribution of the data, are identified and their effects on the results of the presented method are evaluated using synthetic data. Finally, the method is tested using the example corpus, and the results are examined briefly. The method is found to be adversely affected especially by the uneven temporal distribution of the newspaper data. Without additional processing, or improving the quality of the examined data, a significant amount of the detected steps are due to the noise in the data. Various ways of alleviating the effect are proposed, among other suggested improvements on the system.

Nowadays power consumption is a hot and actual domain. Efficient energy consumption allows you to use resources from the environment wisely and, moreover, switch to alternative energy sources where it is possible. This thesis is aimed at analyzing elevator’s power consumption data (average per every 5 minutes and 1 hour). The data has been gathered for several years, so it is a time series. This thesis includes review of time series models, which then can be used for the consequent analysis. Main directions are forecasting power consumption, capturing trends and anomalies. In addition, time series data may also be used for calculating average power consumption for each elevator inside the elevator group. As an outcome, spread of the power consumption across 4 elevators inside the elevator group may be seen. One of the thesis’ goals is to check whether it is even or not.

The semantic shifts in natural language is a well established phenomenon and have been studied for many years. Similarly, the meanings of scientific publications may also change as time goes by. In other words, the same publication may be cited in distinct contexts. To investigate whether the meanings of citations have changed in different scenarios, which is also called in the semantic shifts in citations, we followed the same ideas of how researchers studied semantic shifts in language. To be more specific, we combined the temporal referencing model and the Word2Vec model to explore the semantic shifts of scientific citations in two aspects: their usages over time and their usages across different domains. By observing how citations themselves changed over time and comparing the closest neighbors of citations, we concluded that the semantics of scientific publications did shift in terms of cosine distances.

Audio signals offer invaluable insights into system operational conditions and potential malfunctions. Proactive fault detection in machinery and other infrastructures through audio monitoring provides significant advantages in numerous sectors, such as industrial maintenance, healthcare, and urban security. Localizing anomalies within the spectral content of audio data opens possibilities to not only diagnose but also effectively address the underlying issues. This thesis addresses the challenge of comprehensively capturing the full context of anomalies detected within audio data. To achieve this, we have developed a novel unsupervised method that adapts visual anomaly localization techniques specifically for the analysis of audio data. This approach utilizes visual representations of audio signals, particularly spectrograms, to apply the Student-Teacher Feature Pyramid Matching Method (STFPM) within an unsupervised learning framework. By harnessing the inherent visual patterns in audio data, our method enables precise localization of anomalies. By augmenting the MIMII dataset with synthetic anomalies and conducting extensive testing, we validated our approach’s ability to localize anomalies in audio data. The findings confirm that our model not only detects but also precisely pinpoints the location of these artificially introduced anomalies within audio spectrograms in terms of both time and frequency. This demonstrates the precision and reliability of our approach, highlighting its potential as a promising solution for accurately localizing anomalies in various audio applications.

The Web has become the world's most important application distribution platform, with web pages increasingly containing not static documents, but dynamic, script-driven content. Script-based rendering relies on imperative browser APIs which become unwieldy to use as an application's complexity grows. An increasingly common solution is to use libraries and frameworks which provide an abstraction over rendering and enable a less error-prone declarative programming model. The details of how web frontend frameworks implement rendering vary widely and can potentially have significant consequences for application performance. Frameworks' rendering strategies are typically invisible to the application developer, and may consequently be poorly understood despite their potential impact. In this thesis, we review rendering strategies used in a number of influential and popular web frontend frameworks. By studying their implementation details, we discover ways to categorize and estimate rendering strategies' performance based on input sizes in update loops. To verify and measure the effects of these differences, we implement a number of benchmarks that measure different aspects of rendering. In our benchmarks, we discover significant performance differences ranging up to an order of magnitude under some conditions. Additionally, we confirm that categorizing rendering strategies based on input sizes of update loops is an effective way to estimate their relative performance. The best performing rendering strategies are found to be ones which minimize input sizes in update loops using techniques such as compile-time optimization and reactive programming models.

The rapid progress of communication technologies combined with the growing competition for talents and knowledge has made it necessary to reassess the potential of distributed development which has significantly changed the landscape of the IT industry introducing a variety of cooperation models and making notable changes to the software team work environment. Along with this, enterprises pay more attention to teams’ performance improvement, employing emerging management tools for building up efficient software teams, and trying to get the most out of understanding factors which significantly impact a team’s overall performance. The objective of the research is to systematize factors characterizing high-performing software teams; indicate the benefits of global software development (GSD) models positively influencing software teams’ development performance; and study how companies’ strategies can benefit from distributed development approaches in building high-performing software teams. The thesis is designed as a combination of a systematic literature review followed by qualitative research in the form of semi-structured interviews to validate the findings regarding classification of GSD models’ benefits and their influence on the development of high-performing software teams. At a literature review stage, the research (1) introduces a team performance factors’ model reflecting the aspects which impact the effectiveness of development teams; (2) suggests the classification of GSD models based on organizational, legal, and temporal characteristics, and (3) describes the benefits of GSD models which influence the performance of software development teams. Within the empirical part of the study, we refine the classification of GSD models’ benefits based on the qualitative analysis results of semi-structured interviews with practitioners from IT industry, form a comparison table of GSD benefits depending on the model in question, and introduce recommendations for company and team management regarding the application of GSD in building high-performing software teams. IT corporations, to achieve their strategic goals, can enrich their range of available tools for managing high-performing teams by considering the peculiarities of different GSD models. Company and team management should evaluate the advantages of the distributed operational models, and use the potential and benefits of available configurations to increase teams’ performance and build high-performing software teams.