@article{wagner_kavagait_2018, title = {{KAVAGait}: {Knowledge}-{Assisted} {Visual} {Analytics} for {Clinical} {Gait} {Analysis}}, volume = {25}, url = {https://doi.org/10.1109/TVCG.2017.2785271}, doi = {10/ghppzn}, abstract = {In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient’s gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.}, number = {3}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, author = {Wagner, Markus and Slijepcevic, Djordje and Horsak, Brian and Rind, Alexander and Zeppelzauer, Matthias and Aigner, Wolfgang}, year = {2018}, note = {Projekt: KAVA-Time Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Design Study, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Healthcare, Human Gait Analysis, Human-Computer Interaction, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Visual analytics, Wiss. Beitrag, best, best-bhorsak, best-lbaigner, best-lbwagnerm, best-mzeppelzauer, information visualization, knowledge generation, peer-reviewed}, pages = {1528--1542}, } @inproceedings{blumenstein_evaluating_2016, address = {Baltimore, MD, USA}, title = {Evaluating {Information} {Visualization} on {Mobile} {Devices}: {Gaps} and {Challenges} in the {Empirical} {Evaluation} {Design} {Space}}, isbn = {978-1-4503-4818-8}, url = {https://phaidra.fhstp.ac.at/o:4873}, doi = {10/cwc6}, abstract = {With their increasingly widespread use, mobile devices have become a highly relevant target environment for Information Visualization. However, far too little attention has been paid to evaluation of interactive visualization techniques on mobile devices. To fill this gap, this paper provides a structured overview of the commonly used evaluation approaches for mobile visualization. For this, it systematically reviews the scientific literature of major InfoVis and HCI venues and categorizes the relevant work based on six dimensions circumscribing the design and evaluation space for visualization on mobile devices. Based on the 21 evaluations reviewed, reproducibility, device variety and usage environment surface as the three main issues in evaluation of information visualization on mobile devices. To overcome these issues, we argue for a transparent description of all research aspects and propose to focus more on context of usage and technology.}, booktitle = {Proceedings of 2016 {Workshop} on {Beyond} {Time} {And} {Errors}: {Novel} {Evaluation} {Methods} {For} {Visualization}}, publisher = {ACM}, author = {Blumenstein, Kerstin and Niederer, Christina and Wagner, Markus and Schmiedl, Grischa and Rind, Alexander and Aigner, Wolfgang}, year = {2016}, note = {Projekt: KAVA-Time Projekt: Couragierte Gemeinde Projekt: VALID Projekt: VisOnFire}, keywords = {Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, best, best-kblumenstein, best-lbaigner, best-lbwagnerm, evaluation, information visualization, mobile, peer-reviewed}, pages = {125--132}, } @article{stoiber_netflower_2019, title = {netflower: {Dynamic} {Network} {Visualization} for {Data} {Journalists}}, volume = {38}, url = {https://phaidra.fhstp.ac.at/download/o:4838}, doi = {10/ghm4jz}, abstract = {Abstract Journalists need visual interfaces that cater to the exploratory nature of their investigative activities. In this paper, we report on a four-year design study with data journalists. The main result is netflower, a visual exploration tool that supports journalists in investigating quantitative flows in dynamic network data for story-finding. The visual metaphor is based on Sankey diagrams and has been extended to make it capable of processing large amounts of input data as well as network change over time. We followed a structured, iterative design process including requirement analysis and multiple design and prototyping iterations in close cooperation with journalists. To validate our concept and prototype, a workshop series and two diary studies were conducted with journalists. Our findings indicate that the prototype can be picked up quickly by journalists and valuable insights can be achieved in a few hours. The prototype can be accessed at: http://netflower.fhstp.ac.at/}, journal = {Computer Graphics Forum (EuroVis '19)}, author = {Stoiber, Christina and Rind, Alexander and Grassinger, Florian and Gutounig, Robert and Goldgruber, Eva and Sedlmair, Michael and Emrich, Stefan and Aigner, Wolfgang}, month = jun, year = {2019}, note = {Projekt: VALID Projekt: VisOnFire}, keywords = {FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Human-Computer Interaction, Institut für Creative Media Technologies, Visual Computing, Vortrag, Wiss. Beitrag, best, best-cniederer, best-cstoiber, best-fgrassinger, best-lbaigner, peer-reviewed}, } @article{rind_interactive_2013, title = {Interactive {Information} {Visualization} to {Explore} and {Query} {Electronic} {Health} {Records}}, volume = {5}, url = {https://publik.tuwien.ac.at/files/PubDat_214284.pdf}, doi = {10/f3szvd}, number = {3}, urldate = {2019-10-04}, journal = {Foundations and Trends in Human–Computer Interaction}, author = {Rind, Alexander and Wang, Taowei David and Aigner, Wolfgang and Miksch, Silvia and Wongsuphasawat, Krist and Plaisant, Catherine and Shneiderman, Ben}, year = {2013}, note = {00000}, keywords = {Extern, best-arind, best-lbaigner}, pages = {207--298}, } @inproceedings{wagner_survey_2015, address = {Cagliari, Italy}, title = {A {Survey} of {Visualization} {Systems} for {Malware} {Analysis}}, url = {http://mc.fhstp.ac.at/supp/EuroVisStar2015}, doi = {10/cwc4}, abstract = {Due to the increasing threat from malicious software (malware), monitoring of vulnerable systems is becoming increasingly important. The need to log and analyze activity encompasses networks, individual computers, as well as mobile devices. While there are various automatic approaches and techniques available to detect, identify, or capture malware, the actual analysis of the ever-increasing number of suspicious samples is a time-consuming process for malware analysts. The use of visualization and highly interactive visual analytics systems can help to support this analysis process with respect to investigation, comparison, and summarization of malware samples. Currently, there is no survey available that reviews available visualization systems supporting this important and emerging field. We provide a systematic overview and categorization of malware visualization systems from the perspective of visual analytics. Additionally, we identify and evaluate data providers and commercial tools that produce meaningful input data for the reviewed malware visualization systems. This helps to reveal data types that are currently underrepresented, enabling new research opportunities in the visualization community.}, booktitle = {Eurographics {Conference} on {Visualization} ({EuroVis}) - {STARs}}, publisher = {The Eurographics Association}, author = {Wagner, Markus and Fischer, Fabian and Luh, Robert and Haberson, Andrea and Rind, Alexander and Keim, Daniel A. and Aigner, Wolfgang}, editor = {Borgo, Rita and Ganovelli, Fabio and Viola, Ivan}, year = {2015}, note = {Projekt: TARGET Projekt: KAVA-Time}, keywords = {Creative Industries, FH SP Cyber Security, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Forschungsgruppe Secure Societies, Institut für Creative Media Technologies, Institut für IT Sicherheitsforschung, Josef Ressel Zentrum TARGET, KAVA-Time, Model/Taxonomy, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, Time-Oriented Data, Visual Computing, Visual analytics, Vortrag, Wiss. Beitrag, best, best-lbaigner, best-lbwagnerm, best-rluh, information visualization, interdisziplinär, malicious software, malware, peer-reviewed, survey, taxonomy, visualization}, pages = {105--125}, } @article{aigner_visualizing_2007, title = {Visualizing {Time}-{Oriented} {Data} - {A} {Systematic} {View}}, volume = {31}, doi = {10/dxs9t9}, abstract = {{\textless}p{\textgreater}The analysis of time-oriented data is an important task in many application scenarios. In recent years, a variety of techniques for visualizing such data have been published. This variety makes it difficult for prospective users to select methods or tools that are useful for their particular task at hand. In this article, we develop and discuss a systematic view on the diversity of methods for visualizing time-oriented data. With the proposed categorization we try to untangle the visualization of time-oriented data, which is such an important concern in Visual Analytics. The categorization is not only helpful for users, but also for researchers to identify future tasks in Visual Analytics.{\textless}/p{\textgreater}}, number = {3}, journal = {Computers \& Graphics}, author = {Aigner, Wolfgang and Miksch, Silvia and Müller, Wolfgang and Schumann, H. and Tominski, C.}, year = {2007}, keywords = {Extern, Time-orienteddata, Visual analytics, Visualization, best-lbaigner}, pages = {401--409}, } @article{niederer_taco_2018, title = {{TACO}: {Visualizing} {Changes} in {Tables} {Over} {Time}}, volume = {24}, doi = {10/ghppzq}, abstract = {Multivariate, tabular data is one of the most common data structures used in many different domains. Over time, tables can undergo changes in both structure and content, which results in multiple versions of the same table. A challenging task when working with such derived tables is to understand what exactly has changed between versions in terms of additions/deletions, reorder, merge/split, and content changes. For textual data, a variety of commonplace "diff" tools exist that support the task of investigating changes between revisions of a text. Although there are some comparison tools which assist users in inspecting differences between multiple table instances, the resulting visualizations are often difficult to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TACO, an interactive comparison tool that visualizes effectively the differences between multiple tables at various levels of detail. With TACO we show (1) the aggregated differences between multiple table versions over time, (2) the aggregated changes between two selected table versions, and (3) detailed changes between the selection. To demonstrate the effectiveness of our approach, we show its application by means of two usage scenarios.}, number = {1}, journal = {IEEE Transactions on Visualization and Computer Graphics (InfoVis ’17)}, author = {Niederer, Christina and Stitz, Holger and Hourieh, Reem and Grassinger, Florian and Aigner, Wolfgang and Streit, Marc}, year = {2018}, note = {Projekt: VisOnFire}, keywords = {Center for Digital Health Innovation, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Wiss. Beitrag, best, best-cniederer, best-cstoiber, best-lbaigner, peer-reviewed}, pages = {677--686}, } @article{aigner_evalbench_2013, title = {{EvalBench}: {A} {Software} {Library} for {Visualization} {Evaluation}}, volume = {32}, copyright = {© 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.}, shorttitle = {{EvalBench}}, url = {http://publik.tuwien.ac.at/files/PubDat_217457.pdf}, doi = {10/f3szvb}, abstract = {It is generally acknowledged in visualization research that it is necessary to evaluate visualization artifacts in order to provide empirical evidence on their effectiveness and efficiency as well as their usability and utility. However, the difficulties of conducting such evaluations still remain an issue. Apart from the required know-how to appropriately design and conduct user studies, the necessary implementation effort for evaluation features in visualization software is a considerable obstacle. To mitigate this, we present EvalBench, an easy-to-use, flexible, and reusable software library for visualization evaluation written in Java. We describe its design choices and basic abstractions of our conceptual architecture and demonstrate its applicability by a number of case studies. EvalBench reduces implementation effort for evaluation features and makes conducting user studies easier. It can be used and integrated with third-party visualization prototypes that need to be evaluated via loose coupling. EvalBench supports both, quantitative and qualitative evaluation methods such as controlled experiments, interaction logging, laboratory questionnaires, heuristic evaluations, and insight diaries.}, language = {en}, number = {3}, urldate = {2013-08-20}, journal = {Computer Graphics Forum}, author = {Aigner, Wolfgang and Hoffmann, Stephan and Rind, Alexander}, year = {2013}, keywords = {Extern, Systems, best-lbaigner, evaluation, information visualization, software library}, pages = {41--50}, } @article{alsallakh_state---art_2015, title = {The {State}-of-the-{Art} of {Set} {Visualization}}, volume = {Early view}, issn = {1467-8659}, url = {http://onlinelibrary.wiley.com/doi/10.1111/cgf.12722/abstract}, doi = {10/cwc5}, abstract = {Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net.}, language = {en}, urldate = {2016-01-12}, journal = {Computer Graphics Forum}, author = {Alsallakh, Bilal and Micallef, Luana and Aigner, Wolfgang and Hauser, Helwig and Miksch, Silvia and Rodgers, Peter}, year = {2015}, note = {Projekt: KAVA-Time}, keywords = {FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Visual Computing, Wiss. Beitrag, best, best-lbaigner, peer-reviewed, visualization}, } @article{rind_task_2016, title = {Task {Cube}: {A} {Three}-{Dimensional} {Conceptual} {Space} of {User} {Tasks} in {Visualization} {Design} and {Evaluation}}, volume = {15}, url = {https://publik.tuwien.ac.at/files/PubDat_247156.pdf}, doi = {10/f3szvq}, abstract = {User tasks play a pivotal role in visualization design and evaluation. However, the term ‘task’ is used ambiguously within the visualization community. In this article, we critically analyze the relevant literature and systematically compare definitions for ‘task’ and the usage of related terminology. In doing so, we identify a three-dimensional conceptual space of user tasks in visualization, referred to as task cube, and the more precise concepts ‘objective’ and ‘action’ for tasks. We illustrate the usage of the task cube’s dimensions in an objective-driven visualization process, in different scenarios of visualization design and evaluation, and for comparing categorizations of abstract tasks. Thus, visualization researchers can better formulate their contributions which helps advance visualization as a whole.}, number = {4}, journal = {Information Visualization}, author = {Rind, Alexander and Aigner, Wolfgang and Wagner, Markus and Miksch, Silvia and Lammarsch, Tim}, year = {2016}, note = {Projekt: KAVA-Time Projekt: VALID}, keywords = {Action, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Publikationstyp Schriftpublikation, Visual Computing, Wiss. Beitrag, best, best-arind, best-lbaigner, best-lbwagnerm, design guidelines, interaction, objective, peer-reviewed, task frameworks, task taxonomy, terminology, visualization theory}, pages = {288--300}, } @article{miksch_matter_2014, title = {A {Matter} of {Time}: {Applying} a {Data}-{Users}-{Tasks} {Design} {Triangle} to {Visual} {Analytics} of {Time}-{Oriented} {Data}}, volume = {38}, url = {http://www.ifs.tuwien.ac.at/~silvia/pub/publications/miksch_cag_design-triangle-2014.pdf}, doi = {10/f3szvk}, abstract = {Increasing amounts of data offer great opportunities to promote technological progress and business success. Visual Analytics (VA) aims at enabling the exploration and the understanding of large and complex data sets by intertwining interactive visualization, data analysis, human-computer interaction, as well as cognitive and perceptual science. We propose a design triangle, which considers three main aspects to ease the design: (1) the characteristics of the data, (2) the users, and (3) the users\’ tasks. Addressing the particular characteristics of time and time-oriented data focus the VA methods, but turns the design space into a more complex and challenging one. We demonstrate the applicability of the design triangle by three use cases tackling the time-oriented aspects explicitly. Our design triangle provides a high-level framework, which is simple and very effective for the design process as well as easily applicable for both, researchers and practitioners.}, journal = {Computers \& Graphics}, author = {Miksch, Silvia and Aigner, Wolfgang}, year = {2014}, note = {Projekt: KAVA-Time {\textless}pre wrap=""{\textgreater} Available online 16 November 2013: accepted manuscript (unformatted and unedited PDF): {\textless}a class="moz-txt-link-freetext" href="http://authors.elsevier.com/sd/article/S0097849313001817"{\textgreater}http://authors.elsevier.com/sd/article/S0097849313001817{\textless}/a{\textgreater}{\textless}/pre{\textgreater}}, keywords = {Creative Industries, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Interactive Visualization, Publikationstyp Schriftpublikation, Time-Oriented Data, Visual Computing, Visual analytics, Wiss. Beitrag, best, best-lbaigner, interaction design, peer-reviewed, temporal data mining, visualization}, pages = {286--290}, } @article{aigner_comparative_2012, title = {Comparative {Evaluation} of an {Interactive} {Time}-{Series} {Visualization} that {Combines} {Quantitative} {Data} with {Qualitative} {Abstractions}}, volume = {31}, copyright = {© 2012 The Author(s) Computer Graphics Forum © 2012 The Eurographics Association and Blackwell Publishing Ltd.}, issn = {1467-8659}, url = {http://publik.tuwien.ac.at/files/PubDat_207563.pdf}, doi = {10/f9skn4}, abstract = {In many application areas, analysts have to make sense of large volumes of multivariate time-series data. Explorative analysis of this kind of data is often difficult and overwhelming at the level of raw data. Temporal data abstraction reduces data complexity by deriving qualitative statements that reflect domain-specific key characteristics. Visual representations of abstractions and raw data together with appropriate interaction methods can support analysts in making their data easier to understand. Such a visualization technique that applies smooth semantic zooming has been developed in the context of patient data analysis. However, no empirical evidence on its effectiveness and efficiency is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares this technique with another visualization method used in the well-known KNAVE-II framework. Both methods integrate quantitative data with qualitative abstractions whereas the first one uses a composite representation with color-coding to display the qualitative data and spatial position coding for the quantitative data. The second technique uses juxtaposed representations for quantitative and qualitative data with spatial position coding for both. Results show that the test persons using the composite representation were generally faster, particularly for more complex tasks that involve quantitative values as well as qualitative abstractions.}, language = {en}, number = {3}, urldate = {2012-09-05}, journal = {Computer Graphics Forum}, author = {Aigner, Wolfgang and Rind, Alexander and Hoffmann, Stephan}, year = {2012}, keywords = {Evaluation/methodology, Extern, Information Interfaces and Presentation, User Interfaces, best-lbaigner}, pages = {995--1004}, } @inproceedings{schneider_-plan_2011, title = {A-{Plan}: {Integrating} {Interactive} {Visualization} {With} {Automated} {Planning} for {Cooperative} {Resource} {Scheduling}}, isbn = {978-1-4503-0732-1}, url = {http://publik.tuwien.ac.at/files/PubDat_198182.pdf}, doi = {10/dzvszp}, booktitle = {Proceedings of {International} {Conference} on {Knowledge} {Management} and {Knowledge} {Technologies} ({I}-{KNOW}), {Special} {Track} on {Theory} and {Applications} of {Visual} {Analytics} ({TAVA})}, publisher = {ACM}, author = {Schneider, Thomas and Aigner, Wolfgang}, year = {2011}, keywords = {Extern, Optimization, Resource Scheduling, Visual analytics, best-lbaigner}, pages = {44:1--44:8}, } @article{aigner_visual_2008, title = {Visual {Methods} for {Analyzing} {Time}-{Oriented} {Data}}, volume = {14}, url = {http://mc.fhstp.ac.at/sites/default/files/publications/2007/tvcg/final/aigner_2006_tvcg_vis-time.pdf}, doi = {10/c2qm3s}, abstract = {{\textless}p{\textgreater}Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major concerns \Ð visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.{\textless}/p{\textgreater}}, number = {1}, journal = {IEEE Transactions on Visualization and Computer Graphics}, author = {Aigner, Wolfgang and Miksch, Silvia and Müller, Wolfgang and Schumann, H. and Tominski, C.}, year = {2008}, keywords = {Analysis, Extern, Time-Oriented Data, User, Visualization, best-lbaigner}, pages = {47--60}, } @article{aigner_bertin_2011, title = {Bertin was {Right}: {An} {Empirical} {Evaluation} of {Indexing} to {Compare} {Multivariate} {Time}-{Series} {Data} {Using} {Line} {Plots}}, volume = {30}, copyright = {© 2011 The Authors Computer Graphics Forum © 2011 The Eurographics Association and Blackwell Publishing Ltd.}, issn = {1467-8659}, shorttitle = {Bertin was {Right}}, url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8659.2010.01845.x/abstract}, doi = {10/fjf52x}, abstract = {Line plots are very well suited for visually representing time-series. However, several difficulties arise when multivariate heterogeneous time-series data is displayed and compared visually. Especially, if the developments and trends of time-series of different units or value ranges need to be compared, a straightforward overlay could be visually misleading. To mitigate this, visualization pioneer Jacques Bertin presented a method called indexing that transforms data into comparable units for visual representation. In this paper, we want to provide empirical evidence for this method and present a comparative study of the three visual comparison methods linear scale with juxtaposition, log scale with superimposition and indexing. Although for task completion times, indexing only shows slight advantages, the results support the assumption that the indexing method enables the user to perform comparison tasks with a significantly lower error rate. Furthermore, a post-test questionnaire showed that the majority of the participants favour the indexing method over the two other comparison methods.}, language = {en}, number = {1}, urldate = {2012-09-05}, journal = {Computer Graphics Forum}, author = {Aigner, Wolfgang and Kainz, Christian and Ma, Rui and Miksch, Silvia}, year = {2011}, keywords = {Extern, H.5.2 [Information Interfaces And Presentation (e.g., H.5.2 [Information Interfaces and Presentation (e.g., HCI)]: User Interfaces-Evaluation/methodology, HCI)]: User Interfaces—Evaluation/methodology, best-lbaigner, empirical evaluation, indexing, information visualization, line plot, time-series}, pages = {215--228}, } @inproceedings{federico_role_2017, address = {Paolo Federico and Markus Wagner equally contributed to this paper and are both to be regarded as first authors.}, title = {The {Role} of {Explicit} {Knowledge}: {A} {Conceptual} {Model} of {Knowledge}-{Assisted} {Visual} {Analytics}}, url = {https://publik.tuwien.ac.at/files/publik_261674.pdf}, doi = {10/ghppzr}, abstract = {Visual Analytics (VA) aims to combine the strengths of humans and computers for effective data analysis. In this endeavor, humans’ tacit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the analytic process. While VA environments are starting to include features to formalize, store, and utilize such knowledge, the mechanisms and degree in which these environments integrate explicit knowledge varies widely. Additionally, this important class of VA environments has never been elaborated on by existing work on VA theory. This paper proposes a conceptual model of Knowledge-assisted VA conceptually grounded on the visualization model by van Wijk. We apply the model to describe various examples of knowledge-assisted VA from the literature and elaborate on three of them in finer detail. Moreover, we illustrate the utilization of the model to compare different design alternatives and to evaluate existing approaches with respect to their use of knowledge. Finally, the model can inspire designers to generate novel VA environments using explicit knowledge effectively.}, booktitle = {{IEEE} {Conference} on {Visual} {Analytics} {Science} and {Technology} ({VAST})}, publisher = {IEEE}, author = {Federico, Paolo and Wagner, Markus and Rind, Alexander and Amor-Amorós, Albert and Miksch, Silvia and Aigner, Wolfgang}, year = {2017}, note = {Projekt: KAVA-Time}, keywords = {Center for Digital Health Innovation, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Visual analytics, Vortrag, Wiss. Beitrag, automated analysis, best, best-lbaigner, explicit knowledge, information visualization, peer-reviewed, tacit knowledge, theory and model}, pages = {92--103}, } @article{stitz_thermalplot_2015, title = {{ThermalPlot}: {Visualizing} {Multi}-{Attribute} {Time}-{Series} {Data} {Using} a {Thermal} {Metaphor}}, volume = {22}, issn = {1077-2626}, url = {http://thinkh.github.io/paper-2015-thermalplot/resources/2016_thermalplot_preprint.pdf}, doi = {10/ghppzs}, abstract = {Multi-attribute time-series data plays a vital role in many different domains, such as economics, sensor networks, and biology. An important task when making sense of such data is to provide users with an overview to identify items that show an interesting development over time, including both absolute and relative changes in multiple attributes simultaneously. However, this is not well supported by existing visualization techniques. To address this issue, we present ThermalPlot, a visualization technique that summarizes combinations of multiple attributes over time using an items position, the most salient visual variable. More precisely, the x-position in the ThermalPlot is based on a user-defined degree-of-interest (DoI) function that combines multiple attributes over time. The y-position is determined by the relative change in the DoI value (DDoI) within a user-specified time window. Animating this mapping via a moving time window gives rise to circular movements of items over time—as in thermal systems. To help the user to identify important items that match user-defined temporal patterns and to increase the techniques scalability, we adapt the level of detail of the items representation based on the DoI value. Furthermore, we present an interactive exploration environment for multi-attribute time-series data that ties together a carefully chosen set of visualizations, designed to support analysts in interacting with the ThermalPlot technique. We demonstrate the effectiveness of our technique by means of two usage scenarios that address the visual analysis of economic development data and of stock market data.}, journal = {IEEE Transactions on Visualization and Computer Graphics}, author = {Stitz, Holger and Gratzl, Samuel and Aigner, Wolfgang and Streit, Marc}, year = {2015}, note = {Projekt: KAVA-Time Projekt: VisOnFire}, keywords = {Economics, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Market research, Trajectory, Visual Computing, Visualization, Wiss. Beitrag, animation, best, best-lbaigner, data visualization, encoding, focus+context, multi-attribute data, peer-reviewed, semantic zooming, time-dependent data}, pages = {2594--2607}, } @article{alsallakh_radial_2013, title = {Radial {Sets}: {Interactive} {Visual} {Analysis} of {Large} {Overlapping} {Sets}}, volume = {19}, url = {http://publik.tuwien.ac.at/files/PubDat_219617.pdf}, doi = {10/3v3}, abstract = {{\textless}p{\textgreater}In many applications, data tables contain multi-valued attributes that often store the memberships of the table entities to multiple sets such as which languages a person masters, which skills an applicant documents, or which features a product comes with. With a growing number of entities, the resulting element-set membership matrix becomes very rich of information about how these sets overlap. Many analysis tasks targeted at set-typed data are concerned with these overlaps as salient features of such data. This paper presents Radial Sets, a novel visual technique to analyze set memberships for a large number of elements. Our technique uses frequency-based representations to enable quickly finding and analyzing different kinds of overlaps between the sets, and relating these overlaps to other attributes of the table entities. Furthermore, it enables various interactions to select elements of interest, find out if they are over-represented in specific sets or overlaps, and if they exhibit a different distribution for a specific attribute compared to the rest of the elements. These interactions allow formulating highly-expressive visual queries on the elements in terms of their set memberships and attribute values. As we demonstrate via two usage scenarios, Radial Sets enable revealing and analyzing a multitude of overlapping patterns between large sets, beyond the limits of state-of-the-art techniques.{\textless}/p{\textgreater}}, journal = {IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis)}, author = {Alsallakh, Bilal and Aigner, Wolfgang and Miksch, Silvia and Hauser, Helwig}, year = {2013}, keywords = {Extern, Multi-valued attributes, best-lbaigner, overlapping sets, scalability, set-typed data, visualization technique}, pages = {2496--2505}, } @article{rind_timebench_2013, title = {{TimeBench}: {A} {Data} {Model} and {Software} {Library} for {Visual} {Analytics} of {Time}-{Oriented} {Data}}, volume = {19}, url = {https://publik.tuwien.ac.at/files/PubDat_219700.pdf}, doi = {10/f3szvg}, abstract = {Time-oriented data play an essential role in many Visual Analytics scenarios such as extracting medical insights from collections of electronic health records or identifying emerging problems and vulnerabilities in network traffic. However, many software libraries for Visual Analytics treat time as a flat numerical data type and insufficiently tackle the complexity of the time domain such as calendar granularities and intervals. Therefore, developers of advanced Visual Analytics designs need to implement temporal foundations in their application code over and over again. We present TimeBench, a software library that provides foundational data structures and algorithms for time-oriented data in Visual Analytics. Its expressiveness and developer accessibility have been evaluated through application examples demonstrating a variety of challenges with time-oriented data and long-term developer studies conducted in the scope of research and student projects.}, number = {12}, journal = {IEEE Transactions on Visualization and Computer Graphics}, author = {Rind, Alexander and Lammarsch, Tim and Aigner, Wolfgang and Alsallakh, Bilal and Miksch, Silvia}, year = {2013}, note = {00008}, keywords = {Extern, best-arind, best-lbaigner}, pages = {2247--2256}, } @article{wagner_knowledge-assisted_2017, title = {A knowledge-assisted visual malware analysis system: design, validation, and reflection of {KAMAS}}, issn = {0167-4048}, shorttitle = {A knowledge-assisted visual malware analysis system}, url = {http://www.sciencedirect.com/science/article/pii/S0167404817300263}, doi = {10/b5j9}, abstract = {IT-security experts engage in behavior-based malware analysis in order to learn about previously unknown samples of malicious software (malware) or malware families. For this, they need to find and categorize suspicious patterns from large collections of execution traces. Currently available systems do not meet the analysts' needs which are described as: visual access suitable for complex data structures, visual representations appropriate for IT-security experts, provision of workflow-specific interaction techniques, and the ability to externalize knowledge in the form of rules to ease the analysis process and to share with colleagues. To close this gap, we designed and developed KAMAS, a knowledge-assisted visualization system for behavior-based malware analysis. This paper is a design study that describes the design, implementation, and evaluation of the prototype. We report on the validation of KAMAS with expert reviews, a user study with domain experts and focus group meetings with analysts from industry. Additionally, we reflect on the acquired insights of the design study and discuss the advantages and disadvantages of the applied visualization methods. An interesting finding is that the arc-diagram was one of the preferred visualization techniques during the design phase but did not provide the expected benefits for finding patterns. In contrast, the seemingly simple looking connection line was described as supportive in finding the link between the rule overview table and the rule detail table which are playing a central role for the analysis in KAMAS.}, number = {67}, urldate = {2017-02-17}, journal = {Computers \& Security}, author = {Wagner, Markus and Rind, Alexander and Thür, Niklas and Aigner, Wolfgang}, year = {2017}, note = {Projekt: KAVA-Time}, keywords = {Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Publikationstyp Schriftpublikation, Visual Computing, Visual analytics, Wiss. Beitrag, behavior-based, best, best-lbaigner, best-lbwagnerm, design study, interactive, knowledge generation, malicious software, malware analysis, peer-reviewed, prototype, visualization}, pages = {1--15}, } @article{smuc_score_2009, title = {To {Score} or {Not} to {Score}? {Tripling} {Insights} for {Participatory} {Design}}, volume = {29}, issn = {0272-1716}, url = {http://publik.tuwien.ac.at/files/PubDat_218037.pdf}, doi = {10/cb6vw8}, abstract = {{\textless}br /{\textgreater}}, number = {3}, journal = {IEEE Computer Graphics and Applications}, author = {Smuc, Michael and Mayr, Eva and Lammarsch, Tim and Aigner, Wolfgang and Miksch, Silvia and Gärtner, Johannes}, year = {2009}, keywords = {Extern, Insight, Usability engineering, best-lbaigner, formative evaluat ion, participatory design}, pages = {29--38}, } @article{alsallakh_reinventing_2012, title = {Reinventing the {Contingency} {Wheel}: {Scalable} {Visual} {Analytics} of {Large} {Categorical} {Data}}, volume = {18}, url = {http://publik.tuwien.ac.at/files/PubDat_209191.pdf}, doi = {10/f4fwmj}, abstract = {{\textless}p{\textgreater}Contingency tables summarize the relations between categorical variables and arise in both scientific and business domains. Asymmetrically large two-way contingency tables pose a problem for common visualization methods. The Contingency Wheel has been recently proposed as an interactive visual method to explore and analyze such tables. However, the scalability and readability of this method are limited when dealing with large and dense tables. In this paper we present Contingency Wheel++, new visual analytics methods that overcome these major shortcomings: (1) regarding automated methods, a measure of association based on Pearson\’s residuals alleviates the bias of the raw residuals originally used, (2) regarding visualization methods, a frequency-based abstraction of the visual elements eliminates overlapping and makes analyzing both positive and negative associations possible, and (3) regarding the interactive exploration environment, a multi-level overview+detail interface enables exploring individual data items that are aggregated in the visualization or in the table using coordinated views. We illustrate the applicability of these new methods with a use case and show how they enable discovering and analyzing nontrivial patterns and associations in large categorical data.{\textless}/p{\textgreater}}, number = {12}, journal = {IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE VAST 2012)}, author = {Alsallakh, Bilal and Aigner, Wolfgang and Miksch, Silvia and Gröller, Eduard}, editor = {Lin, Ming and Chen, Min and Drettakis, George}, year = {2012}, keywords = {Extern, Visual analytics, best-lbaigner}, pages = {2849--2858}, } @article{aigner_carevis_2006, title = {{CareVis}: {Integrated} {Visualization} of {Computerized} {Protocols} and {Temporal} {Patient} {Data}}, volume = {37}, url = {http://mc.fhstp.ac.at/sites/default/files/publications/2006/aiim_2006-37-3/aigner_2006_aiim_carevis-preprint.pdf}, doi = {10/cbwjct}, abstract = {{\textless}p{\textgreater}Objective: Currently, visualization support for patient data analysis is mostly limited to the representation of directly measured data. Contextual information on performed treatment steps is an important source to find reasons and explanations for certain phenomena in the measured patient data, but is mostly spared out in the analysis process. This work aims to fill this gap via integrating classical data visualization and visualization of treatment information. Methods and Material: We considered temporal as well as logical data as- pects and applied a user-centered development approach that was guided by user input gathered via a user study, design reviews, and prototype evaluations. Further- more, we investigated the novel PlanningLine glyph, that is used to represent plans in the temporal domain, via a comparative empirical user study. Results: Our interactive visualization approach CareVis provides multiple simul- taneous views to cover different aspects of the complex underlying data structure of treatment plans and patient data. The tightly coupled views use visualization methods well-known to domain experts and are designed to facilitate users\&⋕39; tasks. The views are based on the concepts of clinical algorithm maps and LifeLines which have been extended in order to cope with the powerful and expressive plan rep- resentation language Asbru. Initial feedback of physicians was encouraging and is accompanied by empirical evidence which verifies that PlanningLines are well suited to manage temporal uncertainty. Conclusion: The interactive integration of different visualization methods forms a novel way of combining, relating, and analyzing different kinds of medical data and information that otherwise would be separated.{\textless}/p{\textgreater}}, number = {3}, journal = {Artifical Intelligence in Medicine (AIIM)}, author = {Aigner, Wolfgang and Miksch, Silvia}, year = {2006}, keywords = {Extern, User-Centered Design, best-lbaigner, clinical guidelines, information visualization, patient data, protocol-based care, temporal uncertainty, treatment plans}, pages = {203--218}, } @book{aigner_visualization_2011, address = {London, UK}, series = {Human–{Computer} {Interaction} {Series}}, title = {Visualization of {Time}-{Oriented} {Data}}, isbn = {978-0-85729-078-6}, url = {https://www.springer.com/de/book/9780857290786}, abstract = {Time is an exceptional dimension that is common to many application domains such as medicine, engineering, business, science, biography, history, planning, or project management. Understanding time-oriented data enables us to learn from the past in order to predict, plan, and build the future. Due to the distinct characteristics of time, appropriate visual and analytical methods are required to explore and analyze them.{\textless}/p{\textgreater} {\textless}div class="springerHTML"{\textgreater} {\textless}p{\textgreater}This book starts with an introduction to visualization and a number of historical examples of visual representations. At its core, the book presents and discusses a systematic view of the visualization of time-oriented data. This view is structured along three key questions. While the aspects of time and associated data describe \textit{what} is being visualized, user tasks are related to the question \textit{why} something is visualized. These characteristics and tasks determine \textit{how }the visualization is to be designed. To support visual exploration, interaction techniques and analytical methods are required as well, which are discussed in separate chapters. The concepts explained in this book are illustrated with numerous examples.{\textless}/p{\textgreater} {\textless}p{\textgreater}A large part of this book is devoted to a structured survey of existing techniques for visualizing time and time-oriented data. Overall, 101 different visualization techniques are presented on a per-page basis; each of these self-contained descriptions is accompanied by an illustration and corresponding references.\  This survey serves as a reference for scientists conducting related research as well as for practitioners seeking information on how their time-oriented data can best be visualized in order to gain valuable insights.}, urldate = {2012-08-29}, publisher = {Springer Verlag}, author = {Aigner, Wolfgang and Miksch, Silvia and Schumann, Heidrun and Tominski, Christian}, year = {2011}, doi = {10.1007/978-0-85729-079-3}, keywords = {Extern, best-lbaigner}, } @incollection{aigner_visualization_2015, address = {Boca Raton, Florida, USA}, edition = {2nd}, title = {Visualization {Techniques} for {Time}-{Oriented} {Data}}, isbn = {978-1-4822-5737-3}, url = {https://www.crcpress.com/product/isbn/9781482257373}, booktitle = {Interactive {Data} {Visualization}: {Foundations}, {Techniques}, and {Applications}}, publisher = {A K Peters/CRC Press}, author = {Aigner, Wolfgang and Miksch, Silvia and Schumann, Heidrun and Tominski, Christian}, editor = {Ward, Matthwe O. and Grinstein, Georges and Keim, David}, year = {2015}, note = {eingeladen Projekt: KAVA-Time Projekt: VALID}, keywords = {Center for Digital Health Innovation, Creative Industries, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Time-Oriented Data, Visual Computing, Wiss. Beitrag, best, best-lbaigner, visualization}, pages = {253--284}, } @incollection{aigner_visualization_2008, title = {Visualization {Techniques} to {Support} {Authoring}, {Execution}, and {Maintenance} of {Clinical} {Guidelines}}, url = {http://mc.fhstp.ac.at/sites/default/files/publications/2008/aigner_2008_VisuCGP.pdf}, booktitle = {Computer-{Based} {Medical} {Guidelines} and {Protocols}: {A} {Primer} and {Current} {Trends}}, publisher = {IOS Press, Health Technology and Informatics}, author = {Aigner, Wolfgang and Kaiser, Katharina and Miksch, Silvia}, editor = {ten Teije, Annette and Lucas, Peter and Miksch, Silvia}, year = {2008}, keywords = {Extern, best-lbaigner}, pages = {140--159}, } @incollection{tominski_images_2017, title = {Images of {Time}: {Visual} {Representation} of {Time}-{Oriented} {Data}}, url = {http://mc.fhstp.ac.at/sites/default/files/publications/Tominski17ImagesOfTime.pdf}, booktitle = {Information {Design}: {Research} and {Practice}}, publisher = {Gower/Routledge}, author = {Tominski, Christian and Aigner, Wolfgang and Miksch, Silvia and Schumann, Heidrun}, editor = {Black, A. and Luna, Paul and Lund, O. and Walker, S.}, year = {2017}, note = {Projekt: VisOnFire Projekt: KAVA-Time Projekt: VALID}, keywords = {Center for Digital Health Innovation, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Visual Computing, Wiss. Beitrag, best, best-lbaigner, peer-reviewed}, pages = {23--42}, }