Big Data Visualization Research Papers

08:30-08:40 Opening

08:40-09:30 Keynote

"Big Picture" Mixed-Initiative Visual Analytics of Big Data
Michelle Zhou, IBM Research
[slides]

09:30-10:00 Invited Talk (Cancelled due to US government shutdown)

Data Intensive Visualization and Analysis of Numerically Intensive Applications
Chris Mitchell, Los Alamos National Laboratory

VisReduce: Fast and Responsive Incremental Information Visualization of Large Datasets
Jean-Francois Im, École de technologie supérieure
Félix Giguère Villegas, Mate1.com
Michael J. McGuffin, École de technologie supérieure
[slides]

10:00-10:30 Coffee Break

10:30-12:00 Text Data

Session Chair: Chris Muelder, University of California at Davis

Visualization of Streaming Data: Observing Change and Context in Information Visualization Techniques
Milos Krstajic, Daniel Keim, University of Konstanz
(Presented by Alexander Jaeger)

CompactMap: A Mental Map Preserving Visual Interface for Streaming Text Data
Xiaotong Liu, Ohio State University
Yifan Hu, Stephen North, AT&T Research
Han-Wei Shen, Ohio State University
[slides]

Typograph: Multiscale Spatial Exploration of Text Documents
Alexander Endert, Russ Burtner, Nick Cramer, Ralph Perko, Shawn Hampton, Kristin Cook, Pacific Northwest National Laboratory
(Cancelled due to US government shutdown)
[slides]

12:00-01:30 Lunch

01:30-02:30 Rendering

Overplotting: Unified Solutions under Abstract Rendering
Joseph Cottam, Indiana University
Peter Wang, Continuum Analytics
Andrew Lumsdaine, Indiana University

DriveSense: Contextual Handling of Large-scale Route Map Data for the Automobile
Frederik Wiehr, Saarland University
Vidya Setlur, Nokia Research Center and Tableau Software
Alark Joshi, University of San Francisco
[slides]

02:30-03:30 Visual Analysis

Session Chair: Alark Joshi, University of San Francisco

A Novel Visual Analysis Approach for Clustering Large-Scale Social Data
Zhangye Wang, Wei Chen, Xiajuan Zhou, Chang Chen, Zhejiang University
Ross Maciejewski, Arizona State University
[slides]

Egocentric Storylines for Visual Analysis of Large Dynamic Graphs
Chris Muelder, Tarik Crnovrsanin, University of California at Davis
Arnaud Sallaberry, LIRMM, Universit`e Paul Val´ery Montpellier 3
Kwan-Liu Ma, University of California at Davis
[slides]

03:30-04:00 Coffee Break

04:00-05:30 Scientific Data

Session Chair: Ross Maciejewski, Arizona State University

Visualization of Big SPH Simulations via Compressed Octree Grids
Florian Reichl, Marc Treib, Rüdiger Westermann, Technische Universität München
[slides]

A System for Large-Scale Visualization of Streaming Doppler Data
Peter Kristof, Microsoft
Bedrich Benes, Carol Song, Lan Zhao, Purdue University
[slides]

Dynamic Reduction of Query Result Sets for Interactive Visualization
Leilani Battle, MIT
Remco Chang, Tufts University
Michael Stonebraker, MIT
[slides]

05:30-06:30 Fast, Incremental Visualization

GPU-Accelerated Incremental Correlation Clustering of Large Data with Visual Feedback
Eric Papenhausen, Bing Wang, Stony Brook University
Sungsoo Ha, SUNY Korea
Alla Zelenyuk, Pacific Northwest National Laboratory
Dan Imre, Imre Consulting
Klaus Mueller, Stony Brook University
[slides]

VisReduce: Fast and Responsive Incremental Information Visualization of Large Datasets
Jean-Francois Im, École de technologie supérieure
Félix Giguère Villegas, Mate1.com
Michael J. McGuffin, École de technologie supérieure

This talk has been moved to 9:30am



Introduction

One the major challenges of the Big Data era is that it has realized the availability of a great amount and variety of massive datasets for analysis by non-corporate data analysts, such as research scientists, data journalists, policy makers, SMEs and individuals. A major characteristic of these datasets is that they are: accessible in a raw format that are not being loaded or indexed in a database (e.g., plain text, json, rdf), dynamic, dirty and heterogeneous in nature. The level of difficulty in transforming a data-curious user into someone who can access and analyze that data is even more burdensome now for a great number of users with little or no support and expertise on the data processing part. The purpose of visual data exploration is to facilitate information perception and manipulation, knowledge extraction and inference by non-expert users. The visualization techniques, used in a variety of modern systems, provide users with intuitive means to interactively explore the content of the data, identify interesting patterns, infer correlations and causalities, and supports sense-making activities that are not always possible with traditional data traditional data analysis techniques.

In the Big Data era, several challenges arise in the field of data visualization and analytics. First, the modern exploration and visualization systems should offer scalable data management techniques in order to efficiently handle billion objects datasets, limiting the system response in a few milliseconds. Besides, nowadays systems must address the challenge of on-the-fly scalable visualizations over large and dynamic sets of volatile raw data, offering efficient interactive exploration techniques, as well as mechanisms for information abstraction, sampling and summarization for addressing problems related to visual information overplotting. Further, they must encourage user comprehension offering customization capabilities to different user-defined exploration scenarios and preferences according to the analysis needs. Overall, the challenge is to enable users to gain value and insights out of the data as rapidly as possible, minimizing the role of IT-expert in the loop.

This special issue aims to publish work on multidisciplinary research areas spanning from Data Management and Mining to Information Visualization and Human-Computer Interaction. In addition to the normal submissions, the special issue also considers to select some of the best papers (substantially extended and re-reviewed) from the International Workshop on Big Data Visual Exploration and Analytics (held in conjunction with the 21th Intl. Conference on Extending Database Technology & 21th Intl. Conference on Database Theory - EDBT/ICDT 2018) available here: http://bigvis2018.imis.athena-innovation.gr/

Paper Submission Format and Guidelines

All submitted papers must be clearly written in English and must contain only original work, which has not been published by, or is currently under review for, any other journal, conference, symposium, or workshop. Submissions are expected to not exceed 30 pages (including figures, tables, and references) in the journal's single-column format using 11 point font. Detailed submission guidelines are available under "Guide for Authors" at:

http://www.journals.elsevier.com/big-data-research/

All manuscripts and any supplementary material should be submitted through the Elsevier Editorial System (EES). The authors must select "SI: Big Data Exploration" as Article Type when they reach the Article Type step in the submission process. The EES website is located at:

http://ees.elsevier.com/bdr

All papers will be peer-reviewed by at least three independent reviewers. Requests for additional information should be addressed to the guest editors.

Topics for the Special Issue

Topics of interest include, but are not limited to:

  • Visualization and exploration techniques for various Big Data types (e.g., stream, spatial, high-dimensional, graph)
  • Human-centered database techniques
  • Indexes and data structures for data visualization
  • Raw data visual exploration and analytics
  • Incremental and adaptive processing
  • Interactive caching and prefetching
  • Scalable visual operations (e.g., zooming, panning, linking, brushing)
  • Big Data visual representation techniques (e.g., aggregation, sampling, multi-level, filtering)
  • Setting-oriented visualization (e.g., display resolution/size, smart phones, pixel-oriented, visualization over networks)
  • User-oriented visualization (e.g., assistance, personalization, recommendation)
  • Visual analytics (e.g., pattern matching, timeseries analytics, prediction analysis, outlier detection, OLAP)
  • Visual and interactive data mining
  • Models of human-in-the-loop data analysis
  • High performance/Parallel techniques
  • Visualization hardware and acceleration techniques
  • Linked Data and ontologies visualization
  • Case and user studies
  • Systems and tools

Important Dates

Submission Deadline: August 1, 2018

Author Notification: November 1, 2018

Revised Manuscript Due: January 10, 2019

Notification of Acceptance: February 10, 2019

Final Manuscript Due: February 25, 2019

Tentative Publication Date: May, 2019

Guest Editors

Nikos Bikakis, ATHENA Research Center, Greece

George Papastefanatos, ATHENA Research Center, Greece

Olga Papaemmanouil, Brandeis University, USA

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