KAVA-Time – How man and machine analyze data

Knowledge-Assisted Visual Analytics Methods for Time-Oriented Data. New methods will take advantage of explicit expert knowledge in the Visual Analytics process of data.
Project manager
Partners
  • Fachbereich Informatik und Informationswissenschaft, Universität Konstanz
  • information engineering group, TU Vienna
  • Datenanalyse und Visualisierung, Universität Konstanz
  • Institut für Informatik, Universität Rostock
Funding
FWF – Fonds zur Förderung der wissenschaftlichen Forschung
Runtime
08/01/2013 – 07/31/2018
Status
finished
KAVA-Time: Knowledge-Assisted Visual Analytics Methods for Time-Oriented Data

The visualization of complex data

Analytical reasoning for real world problem solving involves large volumes of uncertain, complex, and often conflicting data that analysts need to make sense of. In this context, time-oriented data is commonplace and plays a special role. Due to the distinct characteristics of time, appropriate methods for exploration and analysis are needed.

For example when displaying lab results in electronic patient records this could mean to show expected value ranges for healthy patients depending on their context such as gender or age. For representing stock price data, the time axis would suppress weekends and bank holidays to avoid a distorted representation of value change. Solutions like these might be achieved by creating specialized applications for each domain and analysis problem at hand. However, this would cause lots of effort and make maintenance and reuse difficult.

To avoid this, Visual Analytics methods which adapt to different contexts and combine interactive visual interfaces with automated analysis methods will be designed. Even though computers have the ability to recognize and visualize patterns in data, they often lack the background knowledge to interpret said data. Therefore, human analysts and their expert knowledge are essential to the process of data analysis.

 

How to take advantage of human expert’s knowledge

Ideally, a Visual Analytics environment would adapt itself to the user’s context and domain specifics of the data to analyse by having access to human knowledge. The basic research project KAVA-Time explores how to take advantage of explicit expert knowledge in the Visual Analytics process to make analytical reasoning more effective and efficient. The project team develops and evaluates knowledge specification methods as well as knowledge-assisted visualization and interaction methods for time-oriented data. This encompasses two main objectives:

  1. to capture analysts' domain knowledge and explorative interests
  2. to take advantage of the explicit knowledge in interaction and visualization methods.The newly integrated specification methods will not only take into account externally given knowledge but also the reuse and sharing of these specifications. Interactive Visual Analytics methods will allow intuitive and direct refinement of explicit knowledge by analysts.

Insights gained by man and machine

Tackling this issue will give rise to more effective environments for gaining insights – the possibility to specify, model, and make use of auxiliary information about data and domain specifics in addition to the raw data, will help to better select, tailor, and adjust appropriate methods for visual representation, interaction, and automated analysis.

Publications

Luh, R., Schramm, G., Wagner, M., Janicke, H., & Schrittwieser, S. (2018). SEQUIN: a grammar inference framework for analyzing malicious system behavior. Journal of Computer Virology and Hacking Techniques, 01–21. https://doi.org/10.1007/s11416-018-0318-x
Bernard, J., Zeppelzauer, M., Sedlmair, M., & Aigner, W. (2018). VIAL – A Unified Process for Visual-Interactive Labeling. The Visual Computer, 16. https://doi.org/10.1007/s00371-018-1500-3
Wagner, M., Slijepcevic, D., Horsak, B., Rind, A., Zeppelzauer, M., & Aigner, W. (2018). KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis. IEEE Transactions on Visualization and Computer Graphics (TVCG), 25(3), 1528–1542. https://doi.org/10.1109/TVCG.2017.2785271
Andrienko, N., Lammarsch, T., Andrienko, G., Fuchs, G., Keim, D. A., Miksch, S., & Rind, A. (2018). Viewing Visual Analytics as Model Building. Computer Graphics Forum, 37(6), 275–299. https://doi.org/10.1111/cgf.13324
Aigner, W., Rind, A., & Wagner, M. (2018). KAVA-Time: Knowledge-Assisted Visual Analytics Methods for Time-Oriented Data. In Tagungsband des 12. Forschungsforum der österreichischen Fachhochschulen (FFH) 2018.
Rind, A., Iber, M., & Aigner, W. (2018). Bridging the Gap Between Sonification and Visualization. In Proc. AVI Workshop on Multimodal Interaction for Data Visualization (MultimodalVis).
Thür, N., Wagner, M., Schick, J., Niederer, C., Eckel, J., Luh, R., & Aigner, W. (2017). A Bigram Supported Generic Knowledge-Assisted Malware Analysis System: BiG2-KAMAS. In Proceedings of the 10th Forum Media Technology 2017 (pp. 107–115). St. Pölten: CEUR-WS.
Schick, J., Wagner, M., Thür, N., Niederer, C., Rottermanner, G., Tavolato, P., & Aigner, W. (2017). Rule Creation in a Knowledge-assisted Visual Analytics Prototype for Malware Analysis. In Proceedings of the 10th Forum Media Technology 2017 (pp. 116–123). St. Pölten: CEUR-WS.
Thür, N., Wagner, M., Schick, J., Niederer, C., Eckel, J., Luh, R., & Aigner, W. (2017). BiG2-KAMAS: Supporting Knowledge-Assisted Malware Analysis with Bi-Gram Based Valuation. In Poster of the 14th Workshop on Visualization for Cyber Security (VizSec). Phoenix, Arizona, USA.
Schick, J., Wagner, M., Thür, N., Niederer, C., Rottermanner, G., Tavolato, P., & Aigner, W. (2017). Supporting Knowledge-assisted Rule Creation in a Behavior-based Malware Analysis Prototype. In Poster of the 14th Workshop on Visualization for Cyber Security (VizSec). Phoenix, Arizona, USA.
Federico, P., Wagner, M., Rind, A., Amor-Amorós, A., Miksch, S., & Aigner, W. (2017). The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics. In IEEE Conference on Visual Analytics Science and Technology (VAST) (pp. 92–103). Paolo Federico and Markus Wagner equally contributed to this paper and are both to be regarded as first authors.: IEEE. https://doi.org/10.1109/VAST.2017.8585498
Blumenstein, K., Niederer, C., Wagner, M., Pfersmann, W., Seidl, M., & Aigner, W. (2017). Visualizing Spatial and Time-Oriented Data in a Second Screen Application. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM.
Rind, A., Haberson, A., Blumenstein, K., Niederer, C., Wagner, M., & Aigner, W. (2017). PubViz: Lightweight Visual Presentation of Publication Data. In B. Kozlíková, T. Schreck, & T. Wischgoll (Eds.), Proc. Eurographics Conf. Visualization (EuroVis) – Short Paper (pp. 169–173). EuroGraphics. https://doi.org/10.2312/eurovisshort.20171152
Wagner, M. (2017). Integrating Explicit Knowledge in the Visual Analytics Process (PhD Thesis). Vienna University of Technology, Vienna.
Aigner, W. (2017, April). Mit Visual Analytics zu Data-Driven Banking. Keynote presented at the Bankensymposium Wachau, Göttweig (Austria).
Wagner, M., Rind, A., Thür, N., & Aigner, W. (2017). A knowledge-assisted visual malware analysis system: design, validation, and reflection of KAMAS. Computers & Security, (67), 1–15. https://doi.org/10.1016/j.cose.2017.02.003
Tominski, C., Aigner, W., Miksch, S., & Schumann, H. (2017). Images of Time: Visual Representation of Time-Oriented Data. In A. Black, P. Luna, O. Lund, & S. Walker (Eds.), Information Design: Research and Practice (pp. 23–42). Gower/Routledge.
Luh, R., Schramm, G., Wagner, M., & Schrittwieser, S. (2017). Sequitur-based Inference and Analysis Framework for Malicious System Behavior. Presented at the First International Workshop on Formal Methods for Security Engineering.
Wagner, M., Sacha, D., Rind, A., Fischer, F., Luh, R., Schrittwieser, S., … Aigner, W. (2017). Visual Analytics: Foundations and Experiences in Malware Analysis. In L. B. Othmane, M. G. Jaatun, & E. Weippl (Eds.), Empirical Research for Software Security: Foundations and Experience (pp. 139–171). CRC/Taylor and Francis.
Bögl, M., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Leite, R. A., Miksch, S., & Rind, A. (2017). Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction. Computer Graphics Forum, 36, 227–238. https://doi.org/10.1111/cgf.13182
Bernard, Jürgen, Zeppelzauer, Matthias, Sedlmair, Michael, & Aigner, Wolfgang. (2017). A Unified Process for Visual-Interactive Labeling. In In Proceedings of the 8th International EuroVis Workshop on Visual Analytics. Barcelona, Spain.
Bernád, Á. Z., Kaiser, M., Mair, S. M., & Rind, A. (2017). Communities in biographischen Netzwerken. In Proceedings of the 10th Forum Media Technology and 3rd All Around Audio Symposium (pp. 83–87). CEUR-WS.
Kromer, L., Wagner, M., Blumenstein, K., Rind, A., & Aigner, W. (2016). Performance Comparison between Unity and D3.js for Cross-Platform Visualization on Mobile Devices. In Proceedings of the 9th Forum Media Technology 2016 (pp. 47–52). CEUR-WS.
Aigner, W. (2016, May). Research Highlights at the Institute of Creative\Media/Technologies. Inivited talk presented at the Chair of Computer Graphics, University of Rostock, Germany.
Aigner, W., & Blumenstein, K. (2016, March). Data Visualisation in Time-Based Media. Workshop presented at the c-tv Konferenz, St. Pölten, Austria.
Blumenstein, K., Niederer, C., Wagner, M., Schmiedl, G., Rind, A., & Aigner, W. (2016). Evaluating Information Visualization on Mobile Devices: Gaps and Challenges in the Empirical Evaluation Design Space. In Proceedings of 2016 Workshop on Beyond Time And Errors: Novel Evaluation Methods For Visualization (pp. 125–132). Baltimore, MD, USA: ACM. https://doi.org/10.1145/2993901.2993906
Wagner, M., Rind, A., Rottermanner, G., Niederer, C., & Aigner, W. (2016). Knowledge-Assisted Rule Building for Malware Analysis. In Proceedings of the 10th Forschungsforum der österreichischen Fachhochschulen. Vienna, Austria: FH des BFI Wien.
Rind, A., Aigner, W., Wagner, M., Miksch, S., & Lammarsch, T. (2016). Task Cube: A Three-Dimensional Conceptual Space of User Tasks in Visualization Design and Evaluation. Information Visualization, 15(4), 288–300. https://doi.org/10.1177/1473871615621602
Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2016). Visual Analytics for Time Series Model Selection, Prediction, and Imputation. In Extended Abstract at Austrian Statistical Days.
Wagner, M., Blumenstein, K., Rind, A., Seidl, M., Schmiedl, G., Lammarsch, T., & Aigner, W. (2016). Native Cross-platform Visualization: A Proof of Concept Based on the Unity3D Game Engine. In Proceedings of International Conference on Information Visualisation (IV16) (p. forthcoming). Lisbon, Portugal: IEEE Computer Society Press.
Ceneda, D., Aigner, W., Bögl, M., Gschwandtner, T., & Miksch, S. (2016). Guiding the Visualization of Time-Oriented Data. In Poster Abstracts of IEEE Conference on Visual Analytics Science and Technology (VAST 2016). Baltimore, MD, USA: IEEE.
Blumenstein, K., Wagner, M., & Aigner, W. (2015). Cross-Platform InfoVis Frameworks for Multiple Users, Screens and Devices: Requirements and Challenges. In DEXiS 2015 Workshop on Data Exploration for Interactive Surfaces. Workshop in conjunction wirth ACM ITS"15. Funchal, Portugal.
Rind, A. (2015, October). Visual Analytics of Health Care Data with a Focus on Time. Invited Talk presented at the International Workshop 2015: Medical records update, Pesaro, Italy.
Blumenstein, K., Wagner, M., Aigner, W., von Suess, R., Prochaska, H., Püringer, J., … Sedlmair, M. (2015). Interactive Data Visualization for Second Screen Applications: State of the Art and Technical Challenges. In H.-J. Schulz, B. Urban, & U. Freiherr von Lukas (Eds.), Proceedings of the International Summer School on Visual Computing (pp. 35–48). Rostock, Germany: Frauenhoferverlag.
Wagner, M., Fischer, F., Luh, R., Haberson, A., Rind, A., Keim, D. A., & Aigner, W. (2015). A Survey of Visualization Systems for Malware Analysis. In R. Borgo, F. Ganovelli, & I. Viola (Eds.), Eurographics Conference on Visualization (EuroVis) - STARs (pp. 105–125). Cagliari, Italy: The Eurographics Association. https://doi.org/10.2312/eurovisstar.20151114
Wagner, M., Aigner, W., Haberson, A., & Rind, A. (2015). Literature review in visual analytics for malware pattern analysis. In Proceedings of the 9th Forschungsforum der österreichischen Fachhochschulen. FH Hagenberg.
Wagner, M. (2015). Integrating Explicit Knowledge in the Visual Analytics Process. In Doctoral Consortium on Computer Vision, Imaging and Computer Graphics Theory and Applications (DCVISIGRAPP 2015). Berlin: SCITEPRESS Digital Library.
Rind, A. (2015, January). Visual Analytics with a Focus on Time. Invited Talk presented at the MAP 2015, CNRS, Marseille, France.
Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2015). Integrating Predictions in Time Series Model Selection. In E. Bertini & J. C. Roberts (Eds.), Proceedings of theEuroVis Workshop on Visual Analytic, EuroVA (pp. 73–77). Eurographics. https://doi.org/10.2312/eurova.20151107
Stitz, H., Gratzl, S., Aigner, W., & Streit, M. (2015). ThermalPlot: Visualizing Multi-Attribute Time-Series Data Using a Thermal Metaphor. IEEE Transactions on Visualization and Computer Graphics, 22, 2594–2607. https://doi.org/10.1109/TVCG.2015.2513389
Aigner, W., Miksch, S., Schumann, H., & Tominski, C. (2015). Visualization Techniques for Time-Oriented Data. In M. O. Ward, G. Grinstein, & D. Keim (Eds.), Interactive Data Visualization: Foundations, Techniques, and Applications (2nd ed., pp. 253–284). Boca Raton, Florida, USA: A K Peters/CRC Press.
Blumenstein, K. (2015). Interactive Mobile Data Visualization for Second Screen. In Doctoral Consortium on Computer Vision, Imaging and Computer Graphics Theory and Applications (DCVISIGRAPP 2015). Berlin: SCITEPRESS Digital Library.
Stitz, H., Gratzl, S., Aigner, W., & Streit, M. (2015). ThermalPlot: Visualizing Multi-Attribute Time-Series Data Using a Thermal Metaphor. In Poster Abstracts of IEEE Conference on Information Visualization (InfoVis "15). Chicago, IL, USA: IEEE.
Bögl, M., Filzmoser, P., Gschwandtner, T., Miksch, S., Aigner, W., Rind, A., & Lammarsch, T. (2015). Visually and Statistically Guided Imputation of Missing Values in Univariate Seasonal Time Series. In Poster Proceedings of the IEEE Visualization Conference 2015.
Alsallakh, B., Micallef, L., Aigner, W., Hauser, H., Miksch, S., & Rodgers, P. (2015). The State-of-the-Art of Set Visualization. Computer Graphics Forum, Early view. https://doi.org/10.1111/cgf.12722
Wagner, M., Aigner, W., Rind, A., Dornhackl, H., Kadletz, K., Luh, R., & Tavolato, P. (2014). Problem Characterization and Abstraction for Visual Analytics in Behavior-Based Malware Pattern Analysis. In L. Harrison (Ed.), Proceedings of the Eleventh Workshop on Visualization for Cyber Security (pp. 9–16). Paris: ACM. https://doi.org/10.1145/2671491.2671498
Alsallakh, B., Micallef, L., Aigner, W., Hauser, H., Miksch, S., & Rodgers, P. (2014). Visualizing Sets and Set-typed Data: State-of-the-Art and Future Challenges. In R. Borgo, R. Maciejewski, & I. Viola (Eds.). Eurographics.
Lammarsch, T., Aigner, W., Bertone, A., Miksch, S., & Rind, A. (2014). Mind the Time: Unleashing Temporal Aspects in Pattern Discovery. Computers & Graphics, 38, 38–50. https://doi.org/10.1016/j.cag.2013.10.007
Rind, A., Aigner, W., Wagner, M., Miksch, S., & Lammarsch, T. (2014). User Tasks for Evaluation: Untangling the Terminology Throughout Visualization Design and Development. In H. Lam, P. Isenberg, T. Isenberg, & M. Sedlmair (Eds.), Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (pp. 9–15). ACM. https://doi.org/10.1145/2669557.2669568
Miksch, S., & Aigner, W. (2014). A Matter of Time: Applying a Data-Users-Tasks Design Triangle to Visual Analytics of Time-Oriented Data. Computers & Graphics, 38, 286–290. https://doi.org/10.1016/j.cag.2013.11.002
Lammarsch, T., Aigner, W., Miksch, S., & Rind, A. (2014). Showing Important Facts to a Critical Audience by Means Beyond Desktop Computing. In Proceedings of the IEEE VIS 2014 Workshop on Envisioning Visualization without Desktop Computing.
Gschwandtner, T., Aigner, W., Miksch, S., Gärtner, J., Kriglstein, S., Pohl, M., & Suchy, N. (2014). TimeCleanser: A Visual Analytics Approach for Data Cleansing of Time-Oriented Data. In S. Lindstaedt, M. Granitzer, & H. Sack (Eds.), 14th International Conference on Knowledge Technologies and Data-driven Business (i-KNOW 2014) (pp. 1–8). ACM Press. https://doi.org/10.1145/2637748.2638423
Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2014). Visual Analytics Methods to Guide Diagnostics for Time Series Model Predictions. In A. Perer, E. Bertini, R. Maciejewski, & J. Sun (Eds.), Proceedings of the IEEE VIS 2014 Workshop Visualization for Predictive Analytics, VPA.

Press coverage

Vorbereiten auf die digitale Welt

Publication date: 16.11.2016
Medium: Der Standard

Wissenschaft und Forschung in Niederösterreich

Publication date: 01.11.2016
Medium: UNIVERSUM Magazin

Big Data – und welche Chancen Daten bieten

Veröffentlichungsdatum: 10.06.2017
Medium: Die Presse

"Landkarte und Kompass für den Datendschungel"

Veröffentlichungsdatum: 30.11.2016
Medium: Der Standard
Autor: Alois Pumhösel

Teamwork zwischen Gehirn und Prozessor

Publication date: 02.06.2014
Medium: Der Standard
Author: Pumhösel Alois

Visual-Analytics-Systeme sollen ihren Benutzern durch eine anschauliche Aufbereitung mehr Übersicht über Datenmaterial geben. Niederösterreichische Forscher entwickeln eine Plattform, in der automatische und menschliche Analyse Hand in Hand gehen.