Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis

A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VISis needed. In this paper, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?" This survey reveals seven main processes where the employment of ML techniques can benefit visualizations:Data Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table

A Visualization Framework for Designing Process Mining Diagrams

Sündmuslogid sisaldavad väärtuslikku informatsiooni äriprotsesside seisundi kohta. Informatsioonile ligi pääsemiseks peab andmestiku viima arusaadavale kujule. Protsissikaeve tööriistad kasutavad erinevaid diagramme, mis toetavad sündmuslogide visuaalset uurimist. Nende diagrammide kujundamine ei ole lihtne ülesanne, sest tihti ei tea arendaja ega kasutaja, kus huvipakkuv informatsioon võib asuda. Seepärast peavad diagrammid olema paindlikud, kuid samas lihtsad ja intuitiivsed, et nii analüütikud kui ka mitteasjatundjad saaksid tööriista kasutada. Antud töö uurib olemasolevate protsessikaeve diagrammide kujundusi ja kuidas need kujundused on autorite poolt põhjendatud. Töös tutvustatakse ka raamistikku, mis on välja töötatud selleks, et lihtsustada ja täiustada protsessikaeve diagrammide kujundamist. See põhineb andmete visualiseerimise teoorial ja visualiseerimise praktikatel protsessikaeves. Raamistiku tõhusust on katsetatud juhtumuuringus.Event logs hold valuable information about the health of business processes. In order to access this information, raw data must be transformed to a comprehensible format. Process mining tools use various diagrams to support visual exploration of process logs. Designing such diagrams is not an easy task because oftentimes neither the developer nor user know where interesting or intriguing information lays. Therefore, the diagrams require thoughtful designs that on the one hand allow flexible exploration, and on the other hand, are simple and intuitive to use for analysts as well as non-experts. This work takes a look into existing solutions of process mining visualizations and the design decisions the visualizations are based on. A framework is proposed to simplify and improve the design process for process mining diagrams. It is based on data visualization theory as well as visualization practices in process mining. The effectiveness of the framework is tested in a case study

Chart recognition and interpretation in document images

Ph.DDOCTOR OF PHILOSOPH

Semi automatic construction progress measurement using a combination of CAD modelling, photogrammetry and construction knowledge

Project managers are lacking up-to-date information about the current stage of the work on the site and they are unable to take corrective measures for the planning variations promptly. It is proposed that the method created within this thesis will reduce this problem greatly by supplying project managers with the data they need to understand schedule and cost variances as early as they occur. This gives them the power to step in and act in good time against the problems by identifying the reasons of the variations much earlier. This thesis is one of the attempts within academia about integrating computer based solutions to monitor and visualise construction progress. Photogrammetric measurements offer reliable results at the cost of more human intervention. This approach offers the possibility of using a hand held camera as a measurement tool. This method also offers complete independence from reliance on the planning and design stage information. Hence, it can be used to re-evaluate, or monitor changes during the project life-cycle. Visible physical body of a superstructure level reinforced concrete frame structure consists of walls, floors, beams, and columns. The building regulations and local construction traditions impose the types and the shapes of these structural elements. The manufacturing industry produces building materials such as bricks and floor blocks in standard sizes. Therefore, it can be seen that knowledge about the design criteria of structural elements or the standard sizes of materials available on the market for construction can be used to create 3D models of building components. A Visual Basic for Applications (VBA) code was created to support these theories and presented in this thesis. The code then was tested and proven to be useful. After comparing the manual measurement results against the outcomes of the case study done for testing the proposed model, it has been revealed that the proposed model can produce 3D model of construction with accurate sizes within similar mistake margins which can be achieved manually

Scientific chart image recognition and interpretation

Ph.DDOCTOR OF PHILOSOPH

Visualization for Recommendation Explainability: A Survey and New Perspectives

Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page

Visualization of Co-authorshipin DIT Arrow

With the popularization of information technology and the unprecedented development of online reading, the management and service of the library are facing severe challenges; the traditional library operation mode has been challenging to optimize the service. At the same time, there is also a fatal impact on library collection and systematic management, however, with the development of visualization techniques in management and service, the library can alleviate the effect of the current network information basically, which achieves the intellectual development of library field. This study empirically provides the evidence to indicate that the force directed layout has the statistically significant performance than the radial layout for visualization of co-authorship in DIT Arrow repository based on the results of surveys

Designing digital constructive visualization tools

The emergence of tools that support fast and easy creation of visualizations has made the benefits of Information Visualization (InfoVis) more accessible. The predominant design for visualization authoring tools often includes features such as automated mappings and visualization templates, which make tools effective and easy-to-use. These features, however, still impose barriers to non-experts (i.e., people with no formal training on visualization concepts). The paradigm of Constructive Visualization (ConstructiveVis) has shown potential to overcome some of these barriers, but it has only been investigated through the use of physical tokens that people manipulate to create representations of data. This dissertation investigates how the principles of ConstructiveVis can be applied in the design and implementation of digital constructive visualization tools. This thesis presents the results of several observational studies that uncover how tools that promote a constructive approach to visualization compare to more conventional ones. It also sheds light on what kind of benefits and limitations digital ConstructiveVis brings into non-experts' visualization design process. The investigations here presented lay the foundations for the design of better visualization tools that not only allow people to create effective visualizations but also promote critical reflection on design principles