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Journal PaperKeshif: Rapid and Expressive Tabular Data Exploration for Novices2017M. Adil Yalçın+Niklas Elmqvist+Benjamin B. BedersonTVCGTransactions on Visualization and Computer Graphics
General purpose graphical interfaces for data exploration are typically based on manual visualization and interaction specifications. While designing manual specification can be very expressive, it demands high efforts to make effective decisions, therefore reducing exploratory speed. Instead, principled automated designs can increase exploratory speed, decrease learning efforts, help avoid ineffective decisions, and therefore better support data analytics novices. Towards these goals, we present Keshif, a new systematic design for tabular data exploration. To summarize a given dataset, Keshif aggregates records by value within attribute summaries, and visualizes aggregate characteristics using a consistent design based on data types. To reveal data distribution details, Keshif features three complementary linked selections: highlighting, filtering, and comparison. Keshif further increases expressiveness through aggregate metrics, absolute/part-of scale modes, calculated attributes, and saved selections, all working in synchrony. Its automated design approach also simplifies authoring of dashboards composed of summaries and individual records from raw data using fluid interaction. We show examples selected from 160+ datasets from diverse domains. Our study with novices shows that after exploring raw data for 15 minutes, our participants reached close to 30 data insights on average, comparable to other studies with skilled users using more complex tools.
Conference Paper
Raising the Bars: Evaluating Treemaps vs. Wrapped Bars for Dense Visualization of Sorted Numeric Data
2017M. Adil Yalçın+Niklas Elmqvist+Benjamin B. BedersonGIGraphics Interface
A standard (single-column) bar chart can effectively visualize a sorted list of numeric records. However, the chart height limits the number of visible records. To show more records, the bars could be made thinner (which could hinder identifying records individu- ally), and scrolling requires interaction to see the overview. Treemaps have been used in practice in non-hierarchical settings for dense visualization of numeric data. Alternatively, we consider wrapped bars, a multi-column bar chart that uses length instead of area to encode numeric values. We compare treemaps and wrapped bars based on their design characteristics, and graphical perception performance for comparison, ranking, and overview tasks using crowdsourced experiments. Our analysis found that wrapped bars perceptually outperform treemaps in all three tasks for dense visualization of non-hierarchical, sorted numeric data.
Conference Paper
AggreSet: Rich and Scalable Set Exploration using Visualizations of Element Aggregations
2015M. Adil Yalçın+Niklas Elmqvist+Benjamin B. BedersonINFOVIS
IEEE Transactions on Visualization and Computer Graphics (Proc. of the VAST / InfoVis / SciVis)
Datasets commonly include multi-value (set-typed) attributes that describe set memberships over elements, such as genres per movie or courses taken per student. Set-typed attributes describe rich relations across elements, sets, and the set intersections. Increasing the number of sets results in a combinatorial growth of relations and creates scalability challenges. Exploratory tasks (e.g. selection, comparison) have commonly been designed in separation for set-typed attributes, which reduces interface consistency. To improve on scalability and to support rich, contextual exploration of set-typed data, we present AggreSet. AggreSet creates aggregations for each data dimension: sets, set-degrees, set-pair intersections, and other attributes. It visualizes the element count per aggregate using a matrix plot for set-pair intersections, and histograms for set lists, set-degrees and other attributes. Its non-overlapping visual design is scalable to numerous and large sets. AggreSet supports selection, filtering, and comparison as core exploratory tasks. It allows analysis of set relations including subsets, disjoint sets and set intersection strength, and also features perceptual set ordering for detecting patterns in set matrices. Its interaction is designed for rich and rapid data exploration. We demonstrate results on a wide range of datasets from different domains with varying characteristics, and report on expert reviews and a case study using student enrollment and degree data with assistant deans at a major public university.
@inproceedings{Yalcin:2016:TMV, author = {Yalcin, Mehmet Adil and Elmqvist, Niklas and Bederson, Benjamin B.}, title = {AggreSet: Rich and Scalable Set Exploration using Visualizations of Element Aggregations}, year = {2016}, month = {January}, journal = {IEEE Transactions on Visualization and Computer Graphics (Proceedings of the Visual Analytics Science and Technology / Information Visualization / Scientific Visualization 2015)}, volume = {22}, number = {01}, pages = {sss-eee}, doi = {xx.xxxx/xxxxxxx.xxxxxxx},}
Conference Poster
Piled Bars: Dense Visualization of Numeric Data2017M. Adil Yalçın+Niklas Elmqvist+Benjamin B. Bederson
GI (Poster Session)
Graphics Interface
Given a limited chart area, increasing the amount of data present- ed while maintaining clarity remains one of the challenges in data visualization. In this paper, we present piled bars, a new tech- nique for dense visualization of numeric data. It is a layered bar chart design where all the bars are sorted and aligned on a single, shared axis. It improves the resolution of the data encoding com- pared to “wrapped bars” (which doesn’t layer the bars and uses multiple axes), by overlapping each bar within a row. In this pa- per, we describe the design of piled bars (including an open source library), and compare it to wrapped bars.
Workshop PaperCognitive Stages in Visual Data Exploration2016M. Adil Yalçın+Niklas Elmqvist+Benjamin B. BedersonBELIV
Proceedings of Beyond Time and Errors: Novel Evaluation Methods for Visualization at IEEE VIS
./academic/Cognitive Stages in Visual Data Exploration.png
./academic/Cognitive Stages in Visual Data Exploration.pdf
Data exploration requires forming analysis goals, planning actions and evaluating results effectively, all of which are complex cognitive activities. Therefore, the data exploration and analysis process can be improved through a principled and comprehensive ap- proach to analyzing the cognitive activities of the user given a data exploration tool. However, many taxonomies and evaluations focus on a specific tool or specific design guides instead of cognitive activities comprehensively. In this paper, we first present the Cognitive Exploration Framework that identifies six stages of cognitive activities in visual data exploration. These stages are a combination of two activities—planning and assessing—across data analysis, interaction, and visualization. Cognitive barriers in each stage can lower the success and speed of data exploration. The framework also identifies the factors of decision-making, existing knowledge and motivation to influence cognitive activities. We argue that cognitive stages can be supported by improving the design of tools rather than their computing capabilities. We demonstrate how the framework clarifies the structured relationship between design guides to specific cognitive stages. In particular, the framework can also be used to guide evaluation of data exploration tools. To reveal cognitive barriers in each stage, we suggest focusing on the failures instead of success stories, and motivating self-driven open-ended exploration instead of using benchmarked tasks on fixed datasets. With these goals, we studied short-term casual use of an exploratory tool by novices with limited training. Our results reveal cognitive barriers across all stages. We also discuss directions for future research and applications.
Workshop Paper
Keshif: Out-of-the-Box Visual and Interactive Data Exploration Environment
2016M. Adil Yalçın+Niklas Elmqvist+Benjamin B. BedersonVIPVisualization In Practice Workshop at IEEEVIS
Keshif is an open-source, web-based data exploration environment that enables data analytics novices to create effective visual and interactive dashboards and explore relations with minimal learning time, and data analytics experts to explore tabular data in multiple perspectives rapidly with minimal setup time. In this paper, we present a high-level overview of the exploratory features and design characteristics of Keshif, as well as its API and a selection of its implementation specifics. We conclude with a discussion of its use as an open-source project.
Book ChapterInformation Visualization2016M. Adil Yalçın+Catherine Plaisant
Big Data and Social Sciences
CRC Publishing
This chapter will show you how to explore data and communicate results so that data can be turned into interpretable, actionable information. There are many ways of presenting statistical infor- mation that convey content in a rigorous manner. The goal of this chapter is to present an introductory overview of effective visualiza- tion techniques for a range of data types and tasks, and to explore the foundations and challenges of information visualization.
Conference Paper
PixelPie: Maximal Poisson-disk Sampling with Rasterization2013
Cheuk Yiu Ip+M. Adil Yalçın+David Luebke+Amitabh Varshney
HPGProceedings of the 5th High-Performance Graphics Conference
We present PixelPie, a highly parallel geometric formulation of the Poisson-disk sampling problem on the graphics pipeline. Traditionally, generating a distribution by throwing darts and removing conflicts has been viewed as an inherently sequential process. In this paper, we present an efficient Poisson-disk sampling algorithm that uses rasterization in a highly parallel manner. Our technique is an iterative two step process. The first step of each iteration involves rasterization of random darts at varying depths. The second step involves culling conflicted darts. Successive iterations identify and fill in the empty regions to obtain maximal distributions. Our approach maps well to the parallel and optimized graphics functions on the GPU and can be easily extended to perform importance sampling. Our implementation can generate Poisson-disk samples at the rate of nearly 7 million samples per second on a GeForce GTX 580 and is significantly faster than the state-of-the-art maximal Poisson-disk sampling techniques.
@inproceedings{Ip:2013:PMP:2492045.2492047,author = {Ip, Cheuk Yiu and Yal\c{c}in, M. Adil and Luebke, David and Varshney, Amitabh},title = {PixelPie: maximal Poisson-disk sampling with rasterization},booktitle = {Proceedings of the 5th High-Performance Graphics Conference},series = {HPG '13},year = {2013},isbn = {978-1-4503-2135-8},location = {Anaheim, California},pages = {17--26},numpages = {10},url = {},doi = {10.1145/2492045.2492047},acmid = {2492047},publisher = {ACM},address = {New York, NY, USA},keywords = {GPGPU, Poisson-disk sampling, dart throwing, maximal sampling},}
Conference Paper
GPU Algorithms for Diamond-based Multiresolution Terrain Processing
2011M. Adil Yalçın+Kenneth Weiss+Leila De FlorianiEGPGVProceedings of the 11th Eurographics conference on Parallel Graphics and Visualization
We present parallel algorithms for processing, extracting and rendering adaptively sampled regular terrain datasets represented as a multiresolution model defined by a super-square-based diamond hierarchy. This model represents a terrain as a nested triangle mesh generated through a series of longest edge bisections and encoded in an implicit hierarchical structure, which clusters triangles into diamonds and diamonds into super-squares. We decompose the problem into three parallel algorithms for performing: generation of the diamond hierarchy from a regularly distributed terrain dataset, selective refinement on the diamond hierarchy and generation of the corresponding crack-free triangle mesh for processing and rendering. We avoid the data transfer bottleneck common to previous approaches by processing all data entirely on the GPU. We demonstrate that this parallel approach can be successfully applied to interactive terrain visualization with a high tessellation quality on commodity GPUs.
@inproceedings{Yalcin:2011:GAD:2386230.2386247,author={Yal\c{c}in, M. Adil and Weiss, Kenneth and De Floriani, Leila},title={GPU algorithms for diamond-based multiresolution terrain processing},booktitle={Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization},series={EG PGV'11},year={2011},isbn={978-3-905674-32-3},location={Llandudno, UK},pages={121--130},numpages={10},url={},doi={10.2312/EGPGV/EGPGV11/121-130},acmid={2386247},publisher={Eurographics Association},address={Aire-la-Ville, Switzerland, Switzerland},}
Conference Paper
Incorporating Learning Analytics into Basic Course Administration: How to Embrace the Opportunity to Identify Inconsistencies and Inform Responses
Lindsey B. Anderson + Elizabeth E. Gardner + Andrew D. Wolvin + Rowena Kirby-Straker + Adil Yalcin + Ben Bederson
NCA101th Annual meeting of the National Communication Association
Top Paper in Basic Course Division
M.Sc. Thesis
Real-Time Simulation and Visualization of Deformations on Heightfields
2010M. Adil YalçınM.Sc. ThesisBilkent University
The applications of computer graphics raise new expectations, such as realistic rendering, real-time dynamic scenes and physically correct simulations. The aim of this thesis is to investigate these problems on the heightfield structure, an extended 2D model that can be processed efficiently by data-parallel architectures. This thesis presents methods for simulation of deformations on heightfield as caused by triangular objects, physical simulation of objects interacting with heightfield and advanced visualization of deformations. The heightfield is stored in two different resolutions to support fast rendering and precise physical simulations as required. The methods are implemented as part of a large-scale heightfield management system, which applies additional level of detail and culling optimizations for the proposed methods and data structures. The solutions provide real-time interaction and recent graphics hardware (GPU) capabilities are utilized to achieve real-time results. All the methods described in this thesis are demonstrated by a sample application and performance characteristics and results are presented to support the conclusions.$002f$002fSD_ILS$002f809$002fSD_ILS:809689/ada;jsessionid=F4D347EA777850D82A8F970F910A0896?qf=SUBJECT%09Subject%09Computer+graphics.%09Computer+graphics.&rw=36
@inproceedings{Yalcin11_egpgv,author={Yalçın, M. A. and Weiss, K. and De Floriani, L.},title={GPU algorithms for diamond-based multiresolution terrain processing},booktitle={Eurographics Symposium on Parallel Graphics and Visualization},year={2011},address={Bangor, Wales},month={April 10--11}pages={121--130},doi={}}
Book ChapterA Generic Multi-View Rendering Engine Architecture2011M. Adil Yalçın+Tolga Çapın
Game Engine Gems Volume 2
A.K. Peters10.1201/b11333-14
@incollection{ref,author={M. Adil Yalçın and Tolga Çapın},title={A Generic Multiview Rendering Engine Architecture},booktitle={Game Engine Gems 2},editor={Eric Lengyel},publisher={A K Peters},year={2011},pages={179--197}}
Workshop Paper
Route Visualization in Indoor Panoramic Imagery with Open Area Maps
2012Mateis Stroila+M. Adil Yalcin+Joe Mays+Narayanan AlwarICMEWIEEE International Conference on Multimedia and Expo Workshops10.1109/ICMEW.2012.93
Route visualization in outdoor panoramic imagery is a very useful feature, available in most map web applications. On the other hand, indoor maps and routing are not yet largely available, even less so related visualizations. In this paper, we present a framework for visualization of indoor points of interest (POIs) and routes. The framework is based on an existing indoor mapping platform for simple creation of navigable floor plans, and comprises tools to manually and semi-automatically align panoramic imagery with the floor plans, and algorithms to select the relevant images and camera orientations for the visualization of the POIs and routes.
@inproceedings{6266434, author={Stroila, M. and Yalcin, A. and Mays, J. and Alwar, N.}, booktitle={Multimedia and Expo Workshops (ICMEW), 2012 IEEE International Conference on}, title={Route Visualization in Indoor Panoramic Imagery with Open Area Maps}, year={2012}, month={July}, pages={499-504}, keywords={Web services;cameras;data visualisation;navigation;POI;camera orientation;floorplan navigation;indoor mapping platform;indoor panoramic image;map Web application;open area map;outdoor panoramic image;points of interest;route visualization;Cameras;Geometry;Image resolution;Navigation;Roads;Routing;Visualization;floorplans;indoor maps;panoramic imagery;routing}, doi={10.1109/ICMEW.2012.93}}
Conference Paper
Editing Heightfield using History Management and 3D Widgets2009M. Adil Yalçın+Tolga ÇapınISCIS24th International Symposium on Computer and Information Sciences
In virtual environments, terrain is generally modeled by heightield, a 2D structure. To be able to create desired terrain geometry, software editors for this specific task have been developed. The graphics hardware, data structures and rendering techniques are developing fast to open up new possibilities to the user and terrain editor functionalities are following such improvements (such as real-time lighting updates during editing operations and multi-texture blending). Yet, current terrain editors mostly fail to give the user feedback about their actions and also fail to help the users understand and undo the editing operations on the terrain. The aim of this study is to investigate the 3d-widget based visualization of possible editing (sculpturing) actions on terrain and to help user undo previous operations.
@inproceedings{Yalcin2009,author={Yalcin, M. Adil and Capin, Tolga K.},title={Editing Heightfield Using History Management And 3d Widgets},booktitle={Computer and Information Sciences, 2009. ISCIS '09. 24th International Symposium on},year={2009},pages={452-457},month={September}}
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