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  1. Book ; Online: Visualization of Time-Oriented Data

    Aigner, Wolfgang / Miksch, Silvia / Schumann, Heidrun / Tominski, Christian

    (Human–Computer Interaction Series)

    2023  

    Author's details by Wolfgang Aigner, Silvia Miksch, Heidrun Schumann, Christian Tominski
    Series title Human–Computer Interaction Series
    Keywords User interfaces (Computer systems) ; Human-computer interaction ; Information visualization
    Language English
    Size 1 Online-Ressource (XIX, 441 p. 307 illus., 250 illus. in color)
    Edition 2nd ed. 2023
    Publisher Springer London ; Imprint: Springer
    Publishing place London
    Document type Book ; Online
    HBZ-ID HT030624285
    ISBN 978-1-4471-7527-8 ; 9781447175261 ; 9781447175285 ; 9781447175292 ; 1-4471-7527-1 ; 1447175263 ; 144717528X ; 1447175298
    DOI 10.1007/978-1-4471-7527-8
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Conference proceedings: Artificial intelligence in medicine

    Miksch, Silvia

    proceedings

    (Lecture notes in computer science ; 3581 : Lecture notes in artificial intelligence)

    2005  

    Event/congress Conference on Artificial Intelligence in Medicine (10, 2005, Aberdeen)
    Author's details 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July, 23 - 27, 2005. Silvia Miksch ... (ed.)
    Series title Lecture notes in computer science ; 3581 : Lecture notes in artificial intelligence
    Collection
    Keywords Artificial Intelligence ; Medical Informatics ; Medizin ; Künstliche Intelligenz
    Subject Artificial intelligence ; Computerunterstützte Intelligenz ; Maschinelle Intelligenz ; KI ; Humanmedizin ; Heilkunst ; Medicine
    Language English
    Size XVII, 547 S. : Ill., graph. Darst.
    Publisher Springer
    Publishing place Berlin u.a.
    Publishing country Germany
    Document type Book ; Conference proceedings
    HBZ-ID HT014441412
    ISBN 978-3-540-27831-3 ; 3-540-27831-1
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2007 A 1824 order for loan Magazin

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  3. Book ; Conference proceedings: Knowledge representation for health care

    Riaño, David / Teije, Annette ten / Miksch, Silvia

    6th International Workshop, KR4HC 2014 ; held as part of the Vienna Summer of Logic, VSL 2014 ; Vienna, Austria, July 21, 2014 ; revised selected papers

    (Lecture notes in artificial intelligence ; 8903)

    2014  

    Event/congress KR4HC (6., 2014, Wien)
    Author's details Silvia Miksch ; David Riaño ; Annette ten Teije ed
    Series title Lecture notes in artificial intelligence ; 8903
    Lecture notes in computer science
    Collection Lecture notes in computer science
    Language English
    Size X, 173 S. : Ill., graph. Darst.
    Publisher Springer
    Publishing place Cham u.a.
    Publishing country Switzerland
    Document type Book ; Conference proceedings
    HBZ-ID HT018522461
    ISBN 978-3-319-13280-8 ; 3-319-13280-6 ; 9783319132815 ; 3319132814
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2015 A 380 available for loan (reading room) Lesesaal EG

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  4. Article ; Online: On Network Structural and Temporal Encodings: A Space and Time Odyssey.

    Filipov, Velitchko / Arleo, Alessio / Bogl, Markus / Miksch, Silvia

    IEEE transactions on visualization and computer graphics

    2023  Volume PP

    Abstract: The dynamic network visualization design space consists of two major dimensions: network structural and temporal representation. As more techniques are developed and published, a clear need for evaluation and experimental comparisons between them emerges. ...

    Abstract The dynamic network visualization design space consists of two major dimensions: network structural and temporal representation. As more techniques are developed and published, a clear need for evaluation and experimental comparisons between them emerges. Most studies explore the temporal dimension and diverse interaction techniques supporting the participants, focusing on a single structural representation. Empirical evidence about performance and preference for different visualization approaches is scattered over different studies, experimental settings, and tasks. This paper aims to comprehensively investigate the dynamic network visualization design space in two evaluations. First, a controlled study assessing participants' response times, accuracy, and preferences for different combinations of network structural and temporal representations on typical dynamic network exploration tasks, with and without the support of standard interaction methods. Second, the best-performing combinations from the first study are enhanced based on participants' feedback and evaluated in a heuristic-based qualitative study with visualization experts on a real-world network. Our results highlight node-link with animation and playback controls as the best-performing combination and the most preferred based on ratings. Matrices achieve similar performance to node-link in the first study but have considerably lower scores in our second evaluation. Similarly, juxtaposition exhibits evident scalability issues in more realistic analysis contexts.
    Language English
    Publishing date 2023-08-30
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2023.3310019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Visual Analytics for Understanding Draco's Knowledge Base.

    Schmidt, Johanna / Pointner, Bernhard / Miksch, Silvia

    IEEE transactions on visualization and computer graphics

    2023  Volume 30, Issue 1, Page(s) 392–402

    Abstract: Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization ... ...

    Abstract Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.
    Language English
    Publishing date 2023-12-25
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2023.3326912
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

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  6. Article ; Online: Guided Visual Analytics for Image Selection in Time and Space.

    Perez-Messina, Ignacio / Ceneda, Davide / Miksch, Silvia

    IEEE transactions on visualization and computer graphics

    2023  Volume 30, Issue 1, Page(s) 66–75

    Abstract: Unexploded Ordnance (UXO) detection, the identification of remnant active bombs buried underground from archival aerial images, implies a complex workflow involving decision-making at each stage. An essential phase in UXO detection is the task of image ... ...

    Abstract Unexploded Ordnance (UXO) detection, the identification of remnant active bombs buried underground from archival aerial images, implies a complex workflow involving decision-making at each stage. An essential phase in UXO detection is the task of image selection, where a small subset of images must be chosen from archives to reconstruct an area of interest (AOI) and identify craters. The selected image set must comply with good spatial and temporal coverage over the AOI, particularly in the temporal vicinity of recorded aerial attacks, and do so with minimal images for resource optimization. This paper presents a guidance-enhanced visual analytics prototype to select images for UXO detection. In close collaboration with domain experts, our design process involved analyzing user tasks, eliciting expert knowledge, modeling quality metrics, and choosing appropriate guidance. We report on a user study with two real-world scenarios of image selection performed with and without guidance. Our solution was well-received and deemed highly usable. Through the lens of our task-based design and developed quality measures, we observed guidance-driven changes in user behavior and improved quality of analysis results. An expert evaluation of the study allowed us to improve our guidance-enhanced prototype further and discuss new possibilities for user-adaptive guidance.
    Language English
    Publishing date 2023-12-25
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2023.3326572
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Visual Parameter Space Exploration in Time and Space.

    Piccolotto, Nikolaus / Bögl, Markus / Miksch, Silvia

    Computer graphics forum : journal of the European Association for Computer Graphics

    2023  Volume 42, Issue 6, Page(s) e14785

    Abstract: Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be ... ...

    Abstract Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models.
    Language English
    Publishing date 2023-04-03
    Publishing country England
    Document type Journal Article
    ZDB-ID 1482655-0
    ISSN 1467-8659 ; 0167-7055
    ISSN (online) 1467-8659
    ISSN 0167-7055
    DOI 10.1111/cgf.14785
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies.

    Filipov, Velitchko / Arleo, Alessio / Miksch, Silvia

    Computer graphics forum : journal of the European Association for Computer Graphics

    2023  Volume 42, Issue 6, Page(s) e14794

    Abstract: Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, ... ...

    Abstract Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever-growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a "roadmap" detailing the current research trends in the field of network visualization. We design our contribution as a meta-survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.
    Language English
    Publishing date 2023-04-04
    Publishing country England
    Document type Journal Article
    ZDB-ID 1482655-0
    ISSN 1467-8659 ; 0167-7055
    ISSN (online) 1467-8659
    ISSN 0167-7055
    DOI 10.1111/cgf.14794
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Show Me Your Face: Towards an Automated Method to Provide Timely Guidance in Visual Analytics.

    Ceneda, Davide / Arleo, Alessio / Gschwandtner, Theresia / Miksch, Silvia

    IEEE transactions on visualization and computer graphics

    2022  Volume 28, Issue 12, Page(s) 4570–4581

    Abstract: Providing guidance during a Visual Analytics session can support analysts in pursuing their goals more efficiently. However, the effectiveness of guidance depends on many factors: Determining the right timing to provide it is one of them. Although in ... ...

    Abstract Providing guidance during a Visual Analytics session can support analysts in pursuing their goals more efficiently. However, the effectiveness of guidance depends on many factors: Determining the right timing to provide it is one of them. Although in complex analysis scenarios choosing the right timing could make the difference between a dependable and a superfluous guidance, an analysis of the literature suggests that this problem did not receive enough attention. In this paper, we describe a methodology to determine moments in which guidance is needed. Our assumption is that the need of guidance would influence the user state-of-mind, as in distress situations during the analytical process, and we hypothesize that such moments could be identified by analyzing the user's facial expressions. We propose a framework composed by a facial recognition software and a machine learning model trained to detect when to provide guidance according to changes of the user facial expressions. We trained the model by interviewing eight analysts during their work and ranked multiple facial features based on their relative importance in determining the need of guidance. Finally, we show that by applying only minor modifications to its architecture, our prototype was able to detect a need of guidance on the fly and made our methodology well suited also for real-time analysis sessions. The results of our evaluations show that our methodology is indeed effective in determining when a need of guidance is present, which constitutes a prerequisite to providing timely and effective guidance in VA.
    MeSH term(s) Computer Graphics ; Algorithms ; Machine Learning ; Software ; Facial Expression
    Language English
    Publishing date 2022-10-26
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2021.3094870
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Influence Maximization With Visual Analytics.

    Arleo, Alessio / Didimo, Walter / Liotta, Giuseppe / Miksch, Silvia / Montecchiani, Fabrizio

    IEEE transactions on visualization and computer graphics

    2022  Volume 28, Issue 10, Page(s) 3428–3440

    Abstract: In social networks, individuals' decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise ... ...

    Abstract In social networks, individuals' decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The Influence Maximization (IM) problem entails selecting a seed set of users that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: (i) a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; (ii) a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall et al. (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms.
    MeSH term(s) Algorithms ; Computer Graphics ; Humans ; Models, Theoretical ; Social Networking ; Stochastic Processes
    Language English
    Publishing date 2022-09-01
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2022.3190623
    Database MEDical Literature Analysis and Retrieval System OnLINE

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