Dr. Ronak Etemadpour

Office: 123, Research I
Address: Campus Ring 1, 28759 Bremen, Germany
Email: r.etemadpour@jacobs-university.de
PhD student
Viscomx (Visual communication and Expertise)

Ronak Etemadpour, Eric Monson, Lars Linsen: The Effect of Stereoscopic Immersive Environments on Projection-based Multi-dimensional Data Visualization, Accepted in iV2013_Vis13 – 5th International Conference on Visualisation.


  • Ronak Etemadpour, Eric Monson, Lars Linsen:
    @PROCEEDINGS {Ronak13,
      author = {Ronak Etemadpour and Eric Monson and Lars Linsen},
      title = {The Effect of Stereoscopic Immersive Environments on Projection-based Multi-dimensional Data Visualization},
      booktitle = {17th International Conference Information Visualisation},
      year = {2013},
      note = {linsenvcgletemadpour},
      url = {http://vcgl.jacobs-university.de/wp-content/uploads/2013/07/cmv_2004.pdf},
      annote = {http://vcgl.jacobs-university.de/wp-content/uploads/2013/07/Etemadpour2013.png},


  • Jorge Poco, Ronak Etemadpour, Fernando V. Paulovich, Tran Van Long, Paul Rosenthal, Maria Cristina Ferreira de Oliveira, Lars Linsen, Rosane Minghim:
    Computer Graphics Forum, vol. 30, iss. 3, pp. 1111–1120
      author = {Jorge Poco and Ronak Etemadpour and Fernando V. Paulovich and Tran Van Long and Paul Rosenthal and Maria Cristina Ferreira de Oliveira and Lars Linsen and Rosane Minghim },
      title = {A Framework for Exploring Multidimensional Data with 3D Projections},
      journal = {Computer Graphics Forum},
      year = {2011},
      volume = {30},
      number = {3},
      pages = {1111--1120},
      note = {etemadpourlongrosenthallinsenvcglsmoothvis},
      annote = {http://vcgl.jacobs-university.de/wp-content/uploads/2011/06/PocoEtAl2011.png},
      url = {http://vcgl.jacobs-university.de/wp-content/uploads/2011/06/PocoEtAl2011_web.pdf},

Perception-based Evaluation of Projection Methods for Multidimensional Data Visualization

Similarity-based layouts generated by multidimensional projections or other dimension reduction techniques are
commonly used to visualize high-dimensional data. Many projection techniques have been recently proposed addressing different objectives and targeted at distinct domains and applications. Nonetheless, very little is known about the effectiveness of the generated layouts from a user’s perspective, how distinct layouts derived from the same data compare regarding the typical tasks supported by such visualizations, or how domain-specific issues affect the outcome of the techniques. Learning more about projection usage is an important step towards both consolidating their role in supporting visual analytics tasks on highdimensional data and taking informed decisions when selecting from alternative techniques.
This work provides a contribution towards this goal. We describe the results of an investigation on the performance of layouts generated by projection techniques as perceived by their users. We conducted a controlled user study (with 61 subjects) to test against the following hypotheses: (1) projection performance is task-dependent; (2) certain projections perform better on certain types of tasks; (3) projection performance depends on the nature of the data; and (4) users prefer projections with good segregation capability. We picked five projection methods representative of different approaches to generate layouts of high-dimensional data. As application domains we investigated image and document data. We identified eight typical tasks, three of them related to segregation capability of the projection, three related to projection precision, and two related to incurred visual cluttering. Answers to questions were compared for correctness against ‘ground truth’ computed directly from the data. We also looked at user confidence and task completion times. Statistical analysis of the collected data resulted in Hypotheses 1 and 3 being confirmed and Hypothesis 2 being confirmed partially, while Hypotheses 4 could not be confirmed. We discuss our findings in comparison with four numerical measures proposed to assess the quality of projection layouts. Moreover, we conducted further studies to investigate the role of orientation and cluster properties of size, shape and density. Our results offer interesting insight on the use of projection layouts in data visualization tasks and provide a departing point for further systematic investigations.

Eye-tracking Investigation During Visual Analysis of Projected Multidimensional Data with 2D Scatterplots

A common strategy for visual encoding of multidimensional data for visual analyses is to use dimensionality reduction. Each multidimensional data point is projected to a 2D point using a certain strategy for the 2D layout. Many layout strategies have been proposed addressing different objectives and targeted at distinct domains and applications. The resulting projected information is typically displayed in form of 2D scatterplots. The user’s perspective such as the role of visual attention and cognitive processing for a respective layout and task has not been addressed much.
It is the goal of this work to investigate, how characteristics in the layout affect the cognitive process during task completion. Eye trackers are an effective means to capture visual attention over time. We use eye tracking in a user study, where we ask users to perform typical analysis tasks for projected multidimensional data such as relation seeking, behavior comparison, and pattern identification. Those tasks often involve detecting and correlating clusters. To understand the role of point density within clusters, cluster sizes, and cluster shapes, we first conducted a study with synthetic 2D scatterplots, where we can set the respective properties manually. We evaluate how changing various parameters affect the visual attention pattern and correlate it to the correctness of the answer. In a second step, we conducted a study where the users were asked to fulfill tasks on real-world data with different characteristics (image collection and document collection) that are visualized using a selection of different dimensionality reduction algorithms. We transfer the insight obtained from synthetic data to investigate the decision making with real-world data. Gestalt laws can be applied to the layout structure. We examine how certain layout techniques produce certain characteristics that change the visual attention pattern. We draw some conclusions on how different projection methods support or hinder decision making leading to respective guidelines.