This thesis deals with the issue of Linked Open Data. The goal of this thesis is to introduce the reader to this issue as a whole and to the possibility of using Linked Open Data for developing useful applications by proposing a new development process focusing on such applications. The theoretical part offers an insight into the issue of Open Data, Linked Open Data and the NoSQL database systems and their usability in this field. It focuses mainly on graph database systems and compares them with relational database systems using predefined criteria. Additionally, the goal of this thesis is to develop an application using the proposed development process, which provides a tool for data presentation and statistical visualisation for open data sets published by the Supreme Audit Office and the Czech Trade Inspection. The application is mainly developed for the purpose of verifying the proposed development process and to demonstrate the connectivity of open data published by two different organizations.The thesis includes the process of selecting a development methodology, which is then used for optimising work on the implementation of the resulting application and the process of selecting a graph database system, that is used to store and modify open data for the purposes of the application.

The purpose of this thesis is to design a Java AST query language and implement tool that uses the query language. This work overviews graph databases and their libraries with focus on Neo4J and Titan. This thesis overviews tools Java bytecode analysis as well. Libraries Procyon and BCEL are described in detail. The work includes a proposal the query language and detailed description of the tool implementation, together with the detailed description of the way how Java entities are stored into the graph databases. In the end, the work deals with experiments and the evaluation of the time complexity of the library.

The amount of data and the development of new technologies used by all society-critical organizations are increasing dramatically. In parallel, data breaches, cyber-attacks, and their devastating consequences are also on the rise, as well as the number of individuals and organizations that are potential targets for such attacks. This places higher demands on security in terms of protecting data against cyber-attacks and controlling access to data that authenticated users want to access. The paper focuses on studying concepts of secure data practices in a GitLab-based cloud environment. The objective is to give answers to questions such as how to ensure the guarantee of secure data and protect it from unauthorized access and changes. The work behind this thesis includes exploring techniques for access control, data encryption, and data immutability. The study is followed by an implementation project that includes fetching code from GitLab verifying user identity and access control, managing data access, and displaying the results. The results of the thesis demonstrate the effectiveness of the implemented security measures in protecting data and controlling access. / Mängden av data och utvecklingen av banbrytande teknologier som idag används av alla samhällsbärande organisationer ökar drastiskt. I samma takt ökar dataintrång, cyberattacker och dess förödande konsekvenser samt antalet personer och organisationer som utgör potentiella offer för sådana typer av attacker. Detta ställer högre krav på säkerheten när det gäller att skydda data mot cyberattacker, men även att kontrollera åtkomsten till data som autentiserade användare vill komma åt. Rapporten fokuserar på att studera hur data säkras i GitLab-baserade molnsystem. Syftet med detta arbete är att ge svar på frågeställningar som till exempel att lova säker åtkomst och skydd för data från obehörig åtkomst och ändringar. Arbetet bakom detta projekt inkluderade undersökning av tekniker som används inom accesskontroll, datakryptering och data-omutlighet. Studien resulterade i en implementation som möjliggör att hämta signerade ändringar (Commits) från GitLab, verifiera användaridentiteten och åtkomstbehörighet, hantera dataåtkomst samt presentera resultaten. Resultaten av detta examensarbete demonstrerar effektiviteten av den implementerade säkerhetsteknikerna i att skydda data och kontrollera access.

Semantic graphs have been used for optimizing various natural language processing tasks as well as augmenting search and information retrieval tasks. In most cases these semantic graphs have been constructed through supervised machine learning methodologies that depend on manually curated ontologies such as Wikipedia or similar. In this thesis, which consists of two parts, we explore in the first part the possibility to automatically populate a semantic graph from an ad hoc data set of 50 000 newspaper articles in a completely unsupervised manner. The utility of the visual representation of the resulting graph is tested on 14 human subjects performing basic information retrieval tasks on a subset of the articles. Our study shows that, for entity finding and document similarity our feature engineering is viable and the visual map produced by our artifact is visually useful. In the second part, we explore the possibility to identify entity relationships in an unsupervised fashion by employing abstractive deep learning methods for sentence reformulation. The reformulated sentence structures are qualitatively assessed with respect to grammatical correctness and meaningfulness as perceived by 14 test subjects. We negatively evaluate the outcomes of this second part as they have not been good enough to acquire any definitive conclusion but have instead opened new doors to explore. / Semantiska grafer har använts för att optimera olika processer för naturlig språkbehandling samt för att förbättra sökoch informationsinhämtningsuppgifter. I de flesta fall har sådana semantiska grafer konstruerats genom övervakade maskininlärningsmetoder som förutsätter manuellt kurerade ontologier såsom Wikipedia eller liknande. I denna uppsats, som består av två delar, undersöker vi i första delen möjligheten att automatiskt generera en semantisk graf från ett ad hoc dataset bestående av 50 000 tidningsartiklar på ett helt oövervakat sätt. Användbarheten hos den visuella representationen av den resulterande grafen testas på 14 försökspersoner som utför grundläggande informationshämtningsuppgifter på en delmängd av artiklarna. Vår studie visar att vår funktionalitet är lönsam för att hitta och dokumentera likhet med varandra, och den visuella kartan som produceras av vår artefakt är visuellt användbar. I den andra delen utforskar vi möjligheten att identifiera entitetsrelationer på ett oövervakat sätt genom att använda abstraktiva djupa inlärningsmetoder för meningsomformulering. De omformulerade meningarna utvärderas kvalitativt med avseende på grammatisk korrekthet och meningsfullhet såsom detta uppfattas av 14 testpersoner. Vi utvärderar negativt resultaten av denna andra del, eftersom de inte har varit tillräckligt bra för att få någon definitiv slutsats, men har istället öppnat nya dörrar för att utforska.

The topic of this master's thesis is to design and implement a web application for visualization of relationships between the MBI objects in order to deliver additional functionality to the web portal of MBI - Management of Business Informatics. The first part introduces the concept of MBI and ranks the functionality of the MBI web portal from the view of user queries and visualization capabilities. The queries which are identified as the most difficult are set as the primary functional request for the new web application. The second part contains the design and implementation of the web application. Both functional and non-functional requirements are specified, including the design of user interface and visual graph searching. Based on these requirements, a suitable application architecture is selected together with a graph database and suitable query library and visualization library. The application is implemented with the selected technologies and tested against the initial requirements. The last part includes a service manual and a user documentation which describes the complete application functionality and provides a use case scenario.

This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.

Zenon AB is an it-company of which, this thesis was made in collaboration with. Zenon AB has clients that generate large amounts of data, therefore it is important for Zenon AB that they make competent choices of database management systems (DBMS) when designing systems for their clients. This thesis will therefore entail research carried out into the comparison of DBMS. Nowadays, there exists a large variety of DBMS. Despite this, there seems to be a lack of comparisons between types of DBMS and therefore a lack clarity of when each type should be used. Thus, this thesis aims to highlight these differences of DBMS types by creating a tailored test for each DBMS type and compare how each type performs in each-others area of specialization. This process will show how big the differences can be and highlight the importance of the choice of DBMS. The time it takes, and how simple DBMSs are to implement seems to be a factor most developers take into consideration when choosing DBMS but there is little research on how to compare the aspect. Therefore, this thesis will investigate the viability of a method to compare how easy the DBMSs are to implement into systems by querying programming help forums such as Stackoverflow. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

In the realm of software engineering, understanding the architecture and measuringthe quality of a source code is essential for maintaining and enhancing softwaresystems. However, many existing tools fall short in evaluating architectural aspects,such as detecting architectural erosion or addressing architecture-related metrics andconstraints tailored to the unique context of their systems or organisations. Thisdeficiency restricts proactive architecture governance and hinders the mitigation ofarchitecture-related risks, creating a critical gap in the analysis of software sourcecode. This thesis presents a novel approach to tackle these challenges. It proposes a graphdatabase as a data structure for analysing the source code and architecture quality andcalculating various architectural metrics of interest. The tool developed in this thesisrepresents the source code structural elements and their relationships in the graphdatabase, enabling an intuitive analysis of the source code architecture. The tool also integrates different code quality metrics parsed from Visual Studiocode metrics results, mapped to their correspondent nodes to assess the source codeoverall quality and identify potential areas of improvement. This empowers softwareengineers and developers to make informed decisions regarding refactoring, codeoptimisation, and architectural enhancements. Furthermore, the tool allows users to define the intended architecture in terms ofmodules to reveal any Architecture erosion (AEr). It also provides the flexibility toestablish custom constraints and metrics through tailored queries, accommodating theunique requirements of their system and company. A case study conducted on a real-world software project validates the effectivenessand usefulness of the proposed approach. The case study demonstrates how the toolanalysis reveals valuable insights into the source code health and identifies patternsthat can impact maintainability and scalability. The results of this research showcasethe potential of our tool as a powerful instrument for analysing the code qualityand architecture of source code, fostering more resilient and sustainable softwaresystems.