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Exploring Big Data Landscapes with Elastic Displays

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We propose a concept to help data analysts to quickly assess parameters and results of cluster algorithms. The presentation and interaction on a flexible display makes it possible to grasp the function- ing of algorithms and focus on the data itself. Two interaction concepts are presented, which demonstrate the strength of elastic displays: a layer concept that allows the recognition of differences between various parameter settings of cluster algorithms, and a Zoomable User Interface, which encourages the in-depth analysis of clusters.

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Exploring Big Data Landscapes with Elastic Displays

  1. 1. 10. Workshop Be-Greifbare Interaktion Konferenz „Mensch und Computer 2017“ Spielend einfach interagieren | Regensburg Exploring Big Data Landscapes with Elastic Displays SEP 10, 2017 Chair of Media Design Technische Universität Dresden Dietrich Kammer, Mandy Keck, Mathias Müller, Thomas Gründer, Rainer Groh
  2. 2. Structure Background Layer Concept Comparison Concept Conclusions Big Data & Clustering, Elastic Displays Variation of Algorithm Parameters Differences between Algorithms Lessons Learned, Future Work
  3. 3. Research Project VANDA - Visual Analytics Interfaces for Big Data Environments Data Analytics, Copyright Observation Data Crawling, Content Exploration Data Analytics and Text Mining for Smart Adaptive Learning Environments Research on Human Computer Interaction and Information Visualization Purchasing Platform bet- ween Businesses with Millions of Products www.vanda-project.de Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 3|13
  4. 4. Clustering 5 Clusters BIRCH Algorithm [Zhang et al., 1996] 15 Clusters Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 4|13
  5. 5. Use Case Data Set Events with 15 attributes Quantitative attributes are normalized to a value between 0 and 10 Glyph Visualization 6 attributes are selected for glyph visualization: price, popularity, time, distance, estimationmusic and category Clusters are mapped to color price popularity time estimation- music distance price popularity time estimation- music distance[Keck et al. 2017] Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 5|13
  6. 6. Elastic Displays – A Global History Force Touch Apple 2015 Herot & Weinzapfel touch-input vector information MIT 1978 Minsky force sensitive screen Atari 1984 KoalaPad KoalaTechnologies 1984 PL-500 Wacom 2000 Flexible Display Plastic Logic 2013 Sinclair Haptic Lens Microsoft Research 1997 Cassinelli Ishikawa Khronos Projector University of Tokyo 2005 Follmer at al. deForm MIT 2011 Yun et al. ElaScreen Seoul University 2013 LG Flex LG Electronics 2014 Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 6|13
  7. 7. Elastic Displays – A Personal History Peschke et al. An Elastic Surface for Tangible Computing DepthTouch AVI 2012 Gründer et al. Towards a Design Space for Elastic Displays CHI 2013 Displays Workshop Franke et al. Interaction in-between 2D and 3D interfaces FlexiWall HCII 2014 Müller et al. FlexiWall: Exploring Layered Data with Elastic Displays ITS 2014 Müller et al. Data Exploration on Elastic Displays using Physical Metaphors xCoAx 2015 Müller at al. A Zoomable Product Browser for Elastic Displays xCoAx 2017 Müller et al. Elastische Displays im Einsatz MuC 2016 Kammer et al. Exploring Big Data Landscapes with Elastic Displays MuC 2017
  8. 8. Layer Concept (1) examine number of clusters (2) select parameter for algorithm (3) select another algorithm Hierarchical Clustering global local Hierarchical Clustering global local K-Means Algorithm global local Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 8|13
  9. 9. Comparison Concept Birch Algorithm K-Means Algorithm (A) comparison of 2 algorithms (B1) zoom in one of these clusters (B2) deep zoom for another LoD Birch Algorithm K-Means Algorithm Birch Algorithm K-Means Algorithm Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 9|13
  10. 10. Demonstration
  11. 11. Conclusions Power of Elastic Displays Intuitive Stacking of Views [Müller, Knöfel, et al., 2014] Natural Zoomable User Interfaces Gestural Interaction with Force Feedback Exploration and Modification of Clustering Algorithms _ More visualization options (e.g. Inertia) _ More interaction possibilities (correct algorithms by editing ground truth data) _ Multi-user scenarios and user identification _ Feedback-loop with calculating backend (cloud computing) Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 11|13
  12. 12. References Agarawala, A. & Balakrishnan, R. (2006). Keepin’ it real: Pushing the desktop metaphor with physics, piles and the pen. In Proceedings of the sigchi conference on human factors in computing systems (pp. 1283–1292). CHI ’06. Montreal, Quebec, Canada: ACM. doi:10.1145/1124772.1124965 Arthur, D. & Vassilvitskii, S. (2007). K-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual acm-siam symposium on discrete algorithms (pp. 1027–1035). SODA ’07. New Orleans: Society for Industrial and Applied Mathematics. Cassinelli, A., Ishikawa, M. Khronos projector. In SIGGRAPH 2005 Emerging technologies, ACM, New York, 2005. Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the second international conference on knowledge discovery and data mining (pp. 226–231). KDD’96. Portland, Oregon: AAAI Press. Frey, B. J. & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976. doi:10.1126/science.1136800 Franke, I. S., Müller, M., Gründer, T., Groh, R. FlexiWall: Interaction in-between 2D and 3D Interfaces. In Proc. HCII 2014, Springer, Berlin 2014. Gründer, T., Kammer, D., Brade, M., & Groh, R. (2013). Towards a design space for elastic displays. In Acm sigchi conference on human factors in computing systems - workshop: Displays take new shape: An agenda for future interactive surfaces. Paris - France. Jacob, R. J., Girouard, A., Hirshfield, L. M., Horn, M. S., Shaer, O., Solovey, E. T., & Zigelbaum, J. (2008). Reality-based interaction: A framework for post-wimp interfaces. In Proceedings of the sigchi conference on human factors in computing systems (pp. 201–210). CHI ’08. Florence, Italy: ACM. doi:10.1145/1357054.1357089 Keck, M., Kammer, D., Gründer, T., Thom, T., Kleinsteuber, M., Maasch, A., & Groh, R. (2017). Towards glyph-based visualizations for big data cluste- ring. In The symposium on visual information communication and interaction - vinci 2017. Bangkok, Thailand (in press). Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 12|13
  13. 13. Müller, M., Gründer, T., & Groh, R. (2015). Data exploration on elastic displays using physical metaphors. In Proceedings xcoax 2015, 25./26. juni, glasgow, schottland. Müller, M., Knöfel, A., Gründer, T., Franke, I. S., & Groh, R. (2014). Flexiwall: Exploring layered data with elastic displays. In Pro- ceedings its 2014, november 16.-19., germany. Ng, A. Y., Jordan, M. I., &Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems 14 (pp. 849–856). MIT Press. Peschke, J., Göbel, F., Gründer, T., Keck, M., Kammer, D., & Groh, R. (2012). Depthtouch: An elastic surface for tangible computing. In Proceedings of the international working conference on advanced visual interfaces (pp. 770–771). AVI ’12. Capri Island, Italy: ACM. doi:10.1145/2254556.2254706 Peschke, J., Göbel, F., Gründer, T., Keck, M., Kammer, D., Groh, R. DepthTouch: An Elastic Surface for Tangible Computing. In Proc. AVI 2012, ACM, New York, 2012. Sibson, R. (1973). Slink: An optimally ecient algorithm for the single-link cluster method. The Computer Journal, 16(1), 30. doi:10.1093/comjnl/16.1.30 Troiano, G. M., Pedersen, E. W., & Hornbæk, K. (2014). User-defined gestures for elastic, deformable displays. In Proceedings of the 2014 internatio- nal working conference on advanced visual interfaces (pp. 1–8). AVI ’14. Como, Italy: ACM. doi:10 . 1145 /2598153.2598184 Watanabe, Y., Cassinelli, A., Komuro, T., Ishikawa, M. The deformable workspace: A membrane between real and virtual space. In Proc TABLETOP 2008, IEEE, 2008. Zhang, T., Ramakrishnan, R., & Livny, M. (1996). Birch: An ecient data clustering method for very large databases. SIGMOD Rec. 25(2), 103–114. doi:10.1145/235968.233324 Workshop Be-Greifbare Interaktion 2017 Sep 10, 2017 D. Kammer et al. 13|13

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