As a result of the digitalization of industrial processes, huge amounts of data are generated in organizations every second. Developing solutions that properly represent this data is critical for making it useful to the organization's stakeholders. Data visualization is a field within computer science that can be utilized to address this demand by enabling the development of appropriate methods for showing the data output of Digital Twins of the Organization (DTO). A recently proposed architecture for a resilient DTO using the EA Blueprint Pattern provides a promising basis for implementing DTOs that are capable of evolving alongside a dynamic organization. However, there is a lack of implementation of any prototypes of the proposed architecture's Information Architecture, which is responsible for dealing with data and visualizing them. Therefore, this thesis aims to develop a visualization prototype, aligned with the proposed architecture. The thesis achieves this goal by first establishing a theoretical basis in data visualization and applicable visualization techniques through a brief review of research articles. After a systematic selection process of visualization tools, the Python frameworks Plotly and Dash are chosen for designing and implementing a visualization prototype. The final design consists of a Python application containing various features and implementing multiple classes which make the prototype easily modifiable and thus able to evolve. In an industrial use case, the prototype is demonstrated to be fulfilling its requirements. Using the observations from the use case, it is finally concluded that the prototype is, in fact, sufficiently aligned with the proposed DTO architecture.

Ett problem med ett programmeringsspråk som har många bibliotek är att det kan vara svårt att identifiera vilket bibliotek som är snabbast. Rapporten bygger på ett experiment där olika Pythonbibliotek jämförs baserat på renderingstiden för diagram med olika datamängder. Stapeldiagram, linjediagram och punktdiagram skapas med Pythonbiblioteken Matlablib, Seaborn och Plotly. Mätningarna visar att det finns en signifikant skillnad på renderingstiderna oavsett vilket bibliotek som används. Mätningarna visar även att det finns en signifikant skillnad, olika bibliotek skalar olika beroende på mängden av data.

The current data visualization provided by European Institute of Gender Equality (EIGE) for their Gender Equality Index (GEI) has been, in literature, proven to be inadequate; among other spatiotemporal visualizations, heatmaps were proven to be an effective alternative. Furthermore, interactivity and responsiveness are both pertinent for ensuring the effectiveness of any visualization. The problem this study aims to solve is the lack of effective and fast performing scalable spatiotemporal data visualizations of the EIGE’s GEI data. The study created four virtually identical heatmaps, using the JavaScript frameworks D3.js and Plotly.js, based on both the original GEI data and an algorithmically scaled GEI data, subsequently measuring the heatmaps’ performance in regards to the load time as well as the responsiveness via user applied filters. D3 was statistically significantly faster than Plotly in all regards. However, Plotly had over 50% less code required to create a virtually identical heatmap compared to D3. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p>