| 1. |
- Riveiro, Maria, 1978-
(författare)
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Visual analytics for maritime anomaly detection
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The surveillance of large sea areas typically involves the analysis of huge quantities of heterogeneous data. In order to support the operator while monitoring maritime traffic, the identification of anomalous behavior or situations that might need further investigation may reduce operators' cognitive load. While it is worth acknowledging that existing mining applications support the identification of anomalies, autonomous anomaly detection systems are rarely used for maritime surveillance. Anomaly detection is normally a complex task that can hardly be solved by using purely visual or purely computational methods. This thesis suggests and investigates the adoption of visual analytics principles to support the detection of anomalous vessel behavior in maritime traffic data. This adoption involves studying the analytical reasoning process that needs to be supported, using combined automatic and visualization approaches to support such process, and evaluating such integration. The analysis of data gathered during interviews and participant observations at various maritime control centers and the inspection of video recordings of real anomalous incidents lead to a characterization of the analytical reasoning process that operators go through when monitoring traffic. These results are complemented with a literature review of anomaly detection techniques applied to sea traffic. A particular statistical-based technique is implemented, tested, and embedded in a proof-of-concept prototype that allows user involvement in the detection process. The quantitative evaluation carried out by employing the prototype reveals that participants who used the visualization of normal behavioral models outperformed the group without aid. The qualitative assessment shows that domain experts are positive towards providing automatic support and the visualization of normal behavioral models, since these aids may reduce reaction time, as well as increase trust and comprehensibility in the system. Based on the lessons learned, this thesis provides recommendations for designers and developers of maritime control and anomaly detection systems, as well as guidelines for carrying out evaluations of visual analytics environments.
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| 2. |
- Stenliden, Linnéa, 1964-
(författare)
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Visual Storytelling Interacting in School : Learning Conditions in the Social Science Classroom
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this compilation thesis is to understand how technology for visual storytelling can be shaped and used in relation to social science education in primary school, but also how social dimensions, technical and other matters create emerging learning conditions in such an educational setting. The visual storytelling technology introduced and used in the study is ‘the Statistics eXplorer platform, a geovisual analytics. The choice of theoretical perspectives to inform and guide the study is a socio-cultural view of human action, but also actor network theory is used to take account also of activities of technology and other matters. The study builds on three empirical materials that generate data from 16 social science teachers, and 126 students from five social science classrooms, in three Swedish primary schools. It contains field notes from the introduction of the technology; focusgroup interviews with teachers; think-aloud interviews with students and two kinds of video recordings from the classrooms (with an ordinary video camera and with software that capture activities at the computer screen, students’ activities and the audio as well). The analysis shows that the visual storytelling technology is shaped in relevant ways for social science teachers. The analysis also illustrates that the visual educational material are usable for primary school students in their social science education. They illustrate further how teachers, students, technology, information, tasks, data types, etc. together and in in close relation create highly complex learning conditions. The technology can therefore be seen as appropriate for the educational practice, but the complexity together with students’ apprehension of how to announce knowledge distribute severe problem spaces in the learning activities. The technology can therefore be assumed as a catalyst for educational change, but to achieve its potentials, reflections on didactic design and knowledge formation is requested to support the quality of students’ knowledge in relation to visual analysis.
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| 3. |
- Johansson Fernstad, Sara, 1980-
(författare)
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Algorithmically Guided Information Visualization : Explorative Approaches for High Dimensional, Mixed and Categorical Data
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. Information visualization provides methods for visual analysis of complex data but, as the amounts of gathered data increase, the challenges of visual analysis become more complex.This thesis presents work utilizing algorithmically extracted patterns as guidance during interactive data exploration processes, employing information visualization techniques. It provides efficient analysis by taking advantage of fast pattern identification techniques as well as making use of the domain expertise of the analyst. In particular, the presented research is concerned with the issues of analysing categorical data, where the values are names without any inherent order or distance; mixed data, including a combination of categorical and numerical data; and high dimensional data, including hundreds or even thousands of variables.The contributions of the thesis include a quantification method, assigning numerical values to categorical data, which utilizes an automated method to define category similarities based on underlying data structures, and integrates relationships within numerical variables into the quantification when dealing with mixed data sets. The quantification is incorporated in an interactive analysis pipeline where it provides suggestions for numerical representations, which may interactively be adjusted by the analyst. The interactive quantification enables exploration using commonly available visualization methods for numerical data. Within the context of categorical data analysis, this thesis also contributes the first user study evaluating the performance of what are currently the two main visualization approaches for categorical data analysis.Furthermore, this thesis contributes two dimensionality reduction approaches, which aim at preserving structure while reducing dimensionality, and provide flexible and user-controlled dimensionality reduction. Through algorithmic quality metric analysis, where each metric represents a structure of interest, potentially interesting variables are extracted from the high dimensional data. The automatically identified structures are visually displayed, using various visualization methods, and act as guidance in the selection of interesting variable subsets for further analysis. The visual representations furthermore provide overview of structures within the high dimensional data set and may, through this, aid in focusing subsequent analysis, as well as enabling interactive exploration of the full high dimensional data set and selected variable subsets. The thesis also contributes the application of algorithmically guided approaches for high dimensional data exploration in the rapidly growing field of microbiology, through the design and development of a quality-guided interactive system in collaboration with microbiologists.
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| 4. |
- Lundblad, Patrik, 1982-
(författare)
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Applied Geovisual Analytics and Storytelling
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 'Geovisual Analytics' represents a cross-disciplinary research that looks for innovative methods to interactively visualize and solve large spatio-temporal related visualization problems for multivariate data through a visual discovery, reasoning and collaborative process. The name emphasizes the link with the well-known research discipline of Visual Analytics and could be viewed as sub-area with its specific focus on space and time posing specific research problems.This thesis focuses on the design, implementation and evaluation of interactive analytical spatio-temporal and multivariate representations demonstrated in several application scenarios which contributes to our understanding of how technology, people and spatial representations of information work effectively together. Data are analysed through the use of coordinated and time-linked views controlled by a time slider. Trends are detected through several visual representations simultaneously, each of which is best suited to highlight different patterns and can help stimulate the analytical visual thinking process so characteristic for geovisual analytics reasoning. Interactive features include tooltips, brushing, highlight, visual inquiry, and conditioned statistics filter mechanisms that can discover outliers and simultaneously update all views.To support knowledge capture and the communication and publishing of gained insights from the data exploration process, a visual storytelling concept with snapshots is introduced where the author can capture the visual data exploration process and share it. Snapshots are memorized interactive visualization views that are captured and later on recreated, so that the reader of the story can see the same mental interactive scenario as the author of the story. These snapshots are then part of a story where the author writes an explanatory text and uses the snapshots to highlight key words. These highlights will allow the reader to recreate the data views used by the author and will guide the reader to the visual discoveries made.The contributions of this thesis are divided into two parts, where the first part includes applications based on geovisual analytics methods for exploring complex weather data and finding patterns and relationships within the data. Earlier, the use of data visualization has been very limited and the introduction of geovisual analytics and the techniques used have significantly improved the visual analysis process as well as increased the flexibility. The results of this research are today used by SMHI to improve optimization and safety of voyages and monitoring of the weather along Swedish roads. Formative evaluations were performed with domain analysts with the purpose to explore qualitative usability issues with respect to visual representations and interactive representation.Furthermore, this thesis contributes with a visual storytelling approach which aims at giving domain experts novel methods for capturing and sharing information discoveries in a way that the reader can follow the process of visual exploration. This approach has been tested and verified within the domain of public statistics, where national and regional statistics is published to the public through the use of embedded interactive visualizations and a story that can engage the reader. The concept of visual storytelling has also been introduced to educators, where stories are used as interactive teaching material for students to make national and regional statistics interactive and visually understandable to the students. It will also challenge the students to investigate new theories and then communicate them visually.
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| 5. |
- Franks, P. W., et al.
(författare)
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Technological readiness and implementation of genomic-driven precision medicine for complex diseases
- 2021
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Ingår i: Journal of Internal Medicine. - : Wiley. - 0954-6820 .- 1365-2796. ; 290:3, s. 602-620
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Forskningsöversikt (refereegranskat)abstract
- The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.
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| 6. |
- Ho, Quan, 1975-
(författare)
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Architecture and Applications of a Geovisual Analytics Framework
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The large and ever-increasing amounts of multi-dimensional, multivariate, multi-source, spatio-temporal data represent a major challenge for the future. The need to analyse and make decisions based on these data streams, often in time-critical situations, demands integrated, automatic and sophisticated interactive tools that aid the user to manage, process, visualize and interact with large data spaces. The rise of `Web 2.0', which is undisputedly linked with developments such as blogs, wikis and social networking, and the internet usage explosion in the last decade represent another challenge for adapting these tools to the Internet to reach a broader user community. In this context, the research presented in this thesis introduces an effective web-enabled geovisual analytics framework implemented, applied and verified in Adobe Flash ActionScript and HTML5/JavaScript. It has been developed based on the principles behind Visual Analytics and designed to significantly reduce the time and effort needed to develop customized web-enabled applications for geovisual analytics tasks and to bring the benefits of visual analytics to the public. The framework has been developed based on a component architecture and includes a wide range of visualization techniques enhanced with various interaction techniques and interactive features to support better data exploration and analysis. The importance of multiple coordinated and linked views is emphasized and a number of effective techniques for linking views are introduced.Research has so far focused more on tools that explore and present data while tools that support capturing and sharing gained insight have not received the same attention. Therefore, this is one of the focuses of the research presented in this thesis. A snapshot technique is introduced, which supports capturing discoveries made during the exploratory data analysis process and can be used for sharing gained knowledge.The thesis also presents a number of applications developed to verify the usability and the overall performance of the framework for the visualization, exploration and analysis of data in different domains. Four application scenarios are presented introducing (1) the synergies among information visualization methods, geovisualization methods and volume data visualization methods for the exploration and correlation of spatio-temporal ocean data, (2) effective techniques for the visualization, exploration and analysis of self-organizing network data, (3) effective flow visualization techniques applied to the analysis of time-varying spatial interaction data such as migration data, commuting data and trade flow data, and (4) effective techniques for the visualization, exploration and analysis of flood data.
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| 7. |
- Johansson, Jimmy
(författare)
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Efficient Information Visualization of Multivariate and Time-Varying Data
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Data can be found everywhere, for example in the form of price, size, weight and colour of all products sold by a company, or as time series of daily observations of temperature, precipitation, wind and visibility from thousands of stations. Due to their size and complexity it is intrinsically hard to form a global overview and understanding of them. Information visualization aims at overcoming these difficulties by transforming data into representations that can be more easily interpreted.This thesis presents work on the development of methods to enable efficient information visualization of multivariate and time-varying data sets by conveying information in a clear and interpretable way, and in a reasonable time. The work presented is primarily based on a popular multivariate visualization technique called parallel coordinates but many of the methods can be generalized to apply to other information visualization techniques.A three-dimensional, multi-relational version of parallel coordinates is presented that enables a simultaneous analysis of all pairwise relationships between a single focus variable and all other variables included in the display. This approach permits a more rapid analysis of highly multivariate data sets. Through a number of user studies the multi-relational parallel coordinates technique has been evaluated against standard, two-dimensional parallel coordinates and been found to better support a number of different types of task.High precision density maps and transfer functions are presented as a means to reveal structure in large data displayed in parallel coordinates. These two approaches make it possible to interactively analyse arbitrary regions in a parallel coordinates display without risking the loss of significant structure.Another focus of this thesis relates to the visualization of time-varying, multivariate data. This has been studied both in the specific application area of system identification using volumetric representations, as well as in the general case by the introduction of temporal parallel coordinates.The methods described in this thesis have all been implemented using modern computer graphics hardware which enables the display and manipulation of very large data sets in real time. A wide range of data sets, both synthetically generated and taken from real applications, have been used to test these methods. It is expected that, as long as the data have multivariate properties, they could be employed efficiently.
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