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Visual Analytics Interfaces for Big Data Environments

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@Workshop Runder Tisch der Technischen Visualistik
17. April 2018

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Visual Analytics Interfaces for Big Data Environments

  1. 1. 20. Runder Tisch der Technischen Visualistik Visual Analytics Interfaces for Big Data Environments 17 April 2018 | Fakultät Informatik Chair of Media Design Technische Universität Dresden Dietrich Kammer & Mandy Keck
  2. 2. Forschungsprojekt 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 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  3. 3. Data Scientist Data Worker End User Future Work Investigate Clustering Determine Parameters Provide Ground Truth Semi-supervised Learning Transparency Controllability Explorability Virtual Reality Elastic Displays
  4. 4. Data Scientist[Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  5. 5. Data Scientist[Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering HD DATA 2D DATA Item 1 Item 2 Item 3 Dimension1 Dimension2 Dimension3 Dimension4 ... Item n Dimension5 Dimensionn ... Item 1 Item 2 Item 3 ... Item n Dimension1 Dimension2 [Munzner 2014] 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  6. 6. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  7. 7. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  8. 8. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering Birch Algorithm global local K-Means Algorithm global local 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  9. 9. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering [Wenskovitch et al. 2018] 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  10. 10. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering Visualization challenges __ Overplotting __ Filtering and grouping 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  11. 11. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering Visualization challenges __ Overplotting __ Filtering and grouping 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  12. 12. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering Visualization challenges __ Overplotting __ Filtering and grouping 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  13. 13. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering Visualization challenges __ Overplotting __ Filtering and grouping Interaction __ Parametric interaction __ Observation-level interaction __ Surface-level interaction 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  14. 14. Data Scientist [Wenskovitch et al. 2018] Tasks __ Explore alternate projections __ Investigate clusters and features __ Individual observations __ Determining parameters for algorithms __ Selection of pipelines for dimension reduction and clustering Visualization challenges __ Overplotting __ Filtering and grouping Interaction __ Parametric interaction __ Observation-level interaction __ Surface-level interaction 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  15. 15. Data Worker [Heimerl et al. 2012] Challenge __ Provide initial ground truth data __ Support of semi-supervised machine learning systems __ Interactive record linkage Approach __ Very small units of work presented in the most suitable way __ Integrate gamification as a tool to make the process more motivating for the user 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  16. 16. End User - Recommender Systems Facebook Netflix Youtube Spotify Pinterest Booking.com
  17. 17. Visualization Challenges[He et al. 2016, Keck & Kammer 2018] Visualization Challenges __ Transparency __ Explorability __ Controllability __ Context-Awareness 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  18. 18. Visualization Challenges[Parra et al. 2014] Visualization Challenges __ Transparency __ Explorability __ Controllability __ Context-Awareness 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  19. 19. Visualization Challenges[Gansner et al. 2009] Visualization Challenges __ Transparency __ Explorability __ Controllability __ Context-Awareness 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  20. 20. Visualization Challenges[Kunkel et al. 2017] Visualization Challenges __ Transparency __ Explorability __ Controllability __ Context-Awareness 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  21. 21. Visualization Challenges[Bogdanov et al. 2013] Visualization Challenges __ Transparency __ Explorability __ Controllability __ Context-Awareness 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  22. 22. Future Work
  23. 23. Future Work [Kammer et al. 2017]
  24. 24. References (1) [Bogdanov et al. 2013] D. Bogdanov, M. Haro, F. Fuhrmann, A. Xambó, E. Gómez, P. Herrera: Semantic audio content-based music recommendation and visualization based on user preference examples, In Information Processing & Management, Volume 49, Issue 1, 2013, Pages 13-33, ISSN 0306-4573, https://doi.org/10.1016/j.ipm.2012.06.004. [He et al. 2016] C. He, D. Parra, K. Verbert: Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities, Expert Systems with Applications, Volume 56, 2016, Pages 9-27, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2016.02.013. [Gansner et al. 2009] E. Gansner, Y. Hu, S. Kobourov, and C. Volinsky: Putting recommendations on the map: visualizing clusters and relations. In Proceedings of the third ACM conference on Recommender systems (RecSys ‚09). ACM, New York, NY, USA, 345-348. 2009. DOI=http://dx.doi.org/10.1145/1639714.1639784 [Heimerl et al. 2012] F. Heimerl, S. Koch, H. Bosch, T. Ertl: Visual Classifier Training for Text Document Retrieval. IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, 2012 [Kammer et al. 2017] D. Kammer, M. Keck, M. Müller, T. Gründer, R. Groh: Exploring Big Data Landscapes with Elastic Displays. Mensch & Computer 2017 - Workshop Begreifbare Interaktion, Oldenbourg Verlag, Regensburg, Germany. 2017 [Keck & Kammer 2018] M. Keck & D. Kammer: On Visualization Challenges for Interactive Recommender Systems. AVI 2018 - VisBIA 2018 - Workshop on Visual Interfaces for Big Data Environments in Industrial Applications, 2018 (in press) [Kunkel et al. 2017] J. Kunkel, B. Loepp, and J. Ziegler: A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI‘17). ACM, New York, NY, USA, 3-15. 2017. DOI: https://doi.org/10.1145/3025171.3025189 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck
  25. 25. [Munzner 2014] T. Munzner: Visualization Analysis and Design. A.K. Peters visualization series. ISBN 978-1-466-50891-0. 2014 http://www.cs.ubc.ca/~tmm/vadbook [Parra et al. 2014] D. Parra, P. Brusilovsky, and C. Trattner. 2014. See what you want to see: visual user-driven approach for hybrid recommendation. In Proceedings of the 19th international conference on Intelligent User Interfaces (IUI ‚14). ACM, New York, NY, USA, 235-240. DOI: http://dx.doi.org/10.1145/2557500.2557542 [Wenskovitch et al. 2018] J. Wenskovitch, I. Crandell, N. Ramakrishnan, L. House, S. Leman, C. North: Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics. IEEE Trans Vis Comput Graph. 2018 Jan, 24(1): 131-141. 2018. doi: 10.1109/TVCG.2017.2745258. References (2) 20. RTTV 17. April 2018 Dietrich Kammer & Mandy Keck

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