ISSN 2186-7437

NII Shonan Meeting Report

No. 2015-17

Big Data Visual Analytics
Seok-Hee HongKwan-Liu Ma
Koji Koyamada
November 8–11, 2015

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Big Data Visual Analytics
Organizers:
Seok-Hee Hong (University of Sydney)
Kwan-Liu Ma (UC Davis)
Koji Koyamada(Kyoto University)
November 8–11, 2015

1

Summary

High-throughput technologies have produced Big Data in many application domains in Science and Engineering including Biomedical Engineering, Genomics,
Software Systems, Computer Networks, Finance, e-commerce, Cyber intelligence, and Homeland Security. The ability to analyze such Big Data for knowledge discovery and decision-making is critical to scientific advancement, businesssuccess, clinical treatments, cyber and national security, and disaster management.
Visual Analytics is the science of analytical reasoning supported by interactive visual techniques, which requires interdisciplinary science integrating techniques from visualization and computer graphics, statistics and mathematics,
data management and knowledge representation, data analysis and machinelearning, cognitive and perceptual sciences.
The main goal of the workshop is to promote Visual Analytics research in
Asia-Pacific region, and form a research community to collaboratively solve
complex problems arising in a variety of application domains. In particular,
special emphasis on the Big Data is addressed.
Big Data Analytics is the biggest and fundamental challenge in IT researchdue to Scalability and Complexity. Innovative scalable techniques for Big Data
Visual Analytics will be the key enabler for researchers and end users in many
application domains and other disciplines.
This meeting aims to bring world-renowned researchers on Visual Analytics and collaboratively develop innovative scalable Visual Analytics solutions
to solve the scalability and complexity issues for analyzing Big Data arisingfrom various application domains including Systems Biology, Social Networks,
Finance, Business intelligence, and Security.
Our specific objectives are:• identify research opportunities in Big Data Visual Analytics, focusing on
the Asia-Pacific context.
• form a broader research community with cross-disciplinary collaboration,
including computer science, information systems, statistics, biology andsociology, with a focus on Visual Analytics of Big Data.

1• foster greater exchange between visualization researchers and practitioners, and to draw more researchers in the Asia-Pacific region to enter this
rapidly growing area of research.
• assist emerging researchers to find linkages to international researchers,
industrial contacts, and competitive research grants and fundings.

2

Program• Nov 7 Saturday Evening– Reception• Nov 8 Sunday Morning– Introduction– Invited Talk 1: Peter Eades– Invited Talk 2: Wei Chen
– Invited Talk 3: Steﬀen Koch
– Group Photo• Nov 8 Sunday Afternoon– Invited Talk 4: Vladimir Batagelj– Invited Talk 5: Shixia Liu– Invited Talk 6: Xiaoru Yuan
– Open Problem Session– Group Formation• Nov 9 Monday Morning– Group Discussion• Nov 9 Monday Afternoon– Group Discussion– Group Report• Nov 10 Tuesday Morning– Group Discussion– Group Report• Nov 10 Tuesday Afternoon– Afternoon: Excursion to Kamakura• Nov 11 Wednesday Morning– Group Discussion– Group Report and Planning– Wrap up
2

3

Invited Talks

How Do You Know Whether Your Visualization Is Good?
Peter Eades, University of Sydney, Australia
The Graph Visualization community has improved the scalability of graphlayout algorithms for many years. However, only recently a discussion has
started to verify the usefulness of established quality metrics, such as the number of edge crossings, in the context of increasingly larger graphs stemming froma variety of application areas such as social network analysis or biology. Initialevidence suggests that the traditional metrics are not well suited to capture thequality of corresponding graph layouts.
This talk proposes a new family of quality metrics for graph visualization;in particular, concentrating on big graphs. I will illustrate these metrics with
examples and apply the metrics to data from previous experiments, leading to
the suggestion that the new metrics are eﬀective.

Challenges for Big Data Visual Analytics
Wei Chen, Zhejiang University, China
Nowadays big data brings us unprecedented opportunities for resolving problems that traditional approaches are not competent and building the next generation IT systems. Despite that data visualization has been applied for many
fields, there remains many challenges. First, data analysis typically search
known patterns from data. How to predict unexpected information remainsa challenging problem. Second, despite many well-established software, toolkits
and systems, a comprehensive visualization scheme and strategy that is adaptable to varied tasks and datasets is demanded. However, a general visualization
software or system is impractical.
Thus, we need to first standardize a visualization and visualization design.
The standardization requires further revolutions for eﬀectively improving theeﬀectiveness and eﬃciency of visualization. Third, urban data is connected tocyber-and-physical spaces. To address practical problems in urban management and security, a suite of schemes are demanded for realizing urban datavisualization.

On the Interplay of Dynamic Data Analysis and Interactive
Visualization
Steﬀen Koch, University of Stuttgart, Germany
This talk focuses on velocity, variability and complexity in the context of
Big Data Visual Analytics. Beyond the sheer mass of data, these Big Datacharacteristics require special attention because many data sources, such as
sensor telemetry or social media, are flooding us with continuous data streams.
Some visual analytics approaches that can handle them have been developed,
but many important questions remain open. In particular the tight integrationof automatic analysis techniques and visualization techniques that can deal with3

incoming data streams has to be improved in order to find better solutions forcontinuous monitoring and real-time situation awareness.
One problematic aspect is the changing data, which is especially diﬃcult
to handle with rigid analysis models that cannot be easily adapted to newsituations. Another issue is that changes have to be reflected with a form ofanimation or the emergence of new data values in the visualization. Humanperception can be hampered by change blindness and inattention blindness,thus requiring the development of adequate means for attention guiding in suchsituations.
The presentation summarizes related works in the field of text and document
visual analytics that address the aspects of velocity, variability and complexity. Many of the problems are pointed out with examples and demos of first
approaches, and open question are formulated accordingly.

On Visualization of Social Networks
Vladimir Batagelj, University of Ljubljana, Slovenia
A network is a graph with additional data on nodes and/or links. In analysis
and visualization of a network they have to be combined. To explore a large
network its overall structure has to be used (as a map) to interactively inspect
interesting details. It would be useful to define a common (JSON based?) layout
format so that independent viewer modules can be developed.

Visual Text Analytics: Towards Better Understanding of
the Textual World
Shixia Liu, Tshinghua University, China
In many big data applications, it is important to survey and explore complex
text data including heterogeneous data and streaming data. A core challenge
is to connect big data with people, that is, to present such complex text data
eﬀectively in an understandable and manageable manner.
This talk presents major challenges in visual analytics of complex text dataand exemplifies them with several text visualization techniques and examples.
It aims at investigating how to best combine and leverage state-of-the-art technologies from multiple fields to help people analyze and understand high volume
text data.

Visualization for Everyone
Xiaoru Yuan, Peking University, China
It is not trivial to provide easy accessible visualization and visual analytics
tools for the general public. In this talk, I will address two sides of this problem.
First, we will discuss how to develop suitable visual analytics approaches undersituation with limited resources; Second, we will discuss visualization and visual
analytical tool for pubic usage, using social media data as examples.

44Group 1: High-Performance Extreme-Scale Visual Analytics

This group focused on the issues concerning the BDVA (Big Data Visual Analytics) within the context of HPC (High Performance Computing) environments.
HPC systems are capable of generating vast amounts of time-varying complex
data sets which can include multivariate, multi-field data, and makes the visual
analytics a challenging task. The group discussion was not limited on how todeal with vast amounts of generated data, but also involved the entire life cycleof the simulation, visualization, and analysis as shown in Fig. 1.
Simulation
Settings

Validation

Visualization
Parameter Settings

Modeling

Exploration

Presentation

Figure 1: Overview of life cycle of the simulation, visualization and analysis

Our discussions were concerned with both the current and expected future
problems faced by the group members within the context of extreme-scale visual
analytics in HPC environments. Many of the common problems are related tothe data movement cost, and the data and parameter space reduction. Considering HPC systems are shared resources, there was a consensus to focus our
discussion on the Batch Mode operations inherent to the HPC environments.
Within this Batch Mode context, the aforementioned problems may be considered in the three scenarios shown in Fig. 2, which also match the life cycle shownin Fig. 1 involving modeling, exploration, and presentation.
Based on Kwan-Liu Ma’s hand drawing illustration

Ɍ

#1
Ƀ
ɻ

Supercomputer#2

Storage#3

Figure 2: three scenarios involving modeling, exploration, and presentation

5

4.1

Scenario 1: Large-Scale Simulation Parameter Space

Conducting a large number of runs of simulations is often required in a wide
range of scientific and engineering studies, such as climate simulation and during
the design process of complex engineering products. In such simulations, normally executed in Batch Mode, extensive parameter sweep calculations might
be required, generating a vast parameter space for exploration and mining. In
this situation, visual analytics can become an important tool for optimizing
the multi-run simulations by assisting in the reduction of simulation parameter
space, and for the selection and extraction of the key parameter set. For this
scenario, the problem and the possible solutions are summarized as follows:
Problem:• The need to reduce the simulation parameter space and identify the key
parameters.
Solutions:• Interactive InfoVis design. [1]• Interactive multiple linked views between physical and abstracted spaces.
• Parameter space reduction. [2]• Uncertainty evaluation.

4.2

Scenario 2: Interactive Exploration of Large-Scale Multivariate Data

The importance of interactive visualization for data exploration and mining was
highlighted in our discussion. To enable interactive navigation over diﬀerent
aspects in large-scale multivariate data sets on the user desktop side, there is
the need to select and pack data subsets of interest on the HPC side. In this
scenario, the main issues are related to tasks of data encoding and selecting theregion of interest, which can be done by extracting some feature representationof the entire data. On the HPC side, the data encoding as well as the generationof diﬀerent projections of the data can be executed in the Batch Mode. For this
scenario, the problem and the possible solutions are summarized as follows:
Problem:• The need/challenge to select and pack data subsets to enable interactive
visualization of large multivariate data.
Solutions:• Feature guided encoding of the data.
– A way to formulate feature descriptions (for computing derived variable(s)).
– Encoding designs.
– Direct visualization processing of the encoded data.
• Batch mode computing of diﬀerent projections of the data. [3][4]6

– User-guided process.
– High-dimensional data visualization.
– Transformations between the projected space and the physical spacefor viewing and selection.
– Views of 4D correlation analysis results and so on.
– Parallelization of all processes.

4.3

Scenario 3: High-Quality and High-Fidelity Visualization

The use of the abundant hardware resources on the HPC side for generating
high-quality and high-fidelity visualization of large-scale data sets has also been
discussed. One of the main challenges in this approach is setting the visualization parameters since the usual trial & error approach would not be feasible
because of the shared nature of the HPC systems. For this scenario, the problems and the possible solutions are summarized as follows:
Problems:• The challenges of setting visualization parameters.
• Trial and error does not work because a supercomputer is a shared resource.
Solutions:• In situ or batch model preprocessing of the data (likely based on sampling)
to derive statistics or overviews of the data.
• “Explorable Images” technique. [5]• History/Provenance driven machine learning to derive/recommend visualization parameters (Machine Learning Group results).

4.4

References[1] S. Liu, W. Cui, Y. Wu, and M. Liu. A Survey on Information Visualization:
Recent Advances and Challenges. The Visual Computer: International Journalof Computer Graphics, 30(12):1373-1393, 2014.
[2] D. Engel, L. Hüttenberger, and B. Hamann. A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization. In IRTG
1131 Workshop 2011, pages 135-149, 2011.
[3] H. N. Miyamura, S. Hayashi, Y. Suzuki, and H. Takemiya. Spatio-Temporal
Mapping -A Technique for Overview Visualization of Time-Series Datasets-.
Progress in Nuclear Science and Technology, 2:603-608, 2011.
[4] S. Liu, B. Wang, J. J. Thiagarajan, P-T. Bremer, and V.Pascucci. Multivariate Volume Visualization through Dynamic Projections. In IEEE LDAV
2014, pages 35-42, 2014.
[5] A. Tikhonova, C. D. Correa, and K-L. Ma, Explorable Images for Visualizing
Volume Data. In IEEE PacificVis 2010, pages 177-184, 2010.

7

5Group 2: Challenges for Interactive Machine
Learning in Big Data Visual Analytics

It is important to properly utilize machine learning components in big datavisual analytics. To this end, however, there exist numerous challenges, whichhinders the eﬀective use of machine learning in visual analytics. We have discussed and enlightened various challenges.
First of all, the pipeline of using machine learning can be summarized asshown in Fig. 3. In this process, user intervention can occur at any stage,
and depending on how we view the machine learning module, one can think of
two diﬀerent perspectives: a white-box or a black-box approach. The former
considers a machine learning module as what users can intervene in the middleof its process while the latter view the machine learning module where users
can only control its input and output. Each perspective has its own pros andcons, and we discussed the following challenges from each perspective.

Figure 3: Overview of a machine learning process

5.1

A Black-box perspective:

1. How to provide an eﬀective mechanism to explore big data for choosing
the right analysis model, such as K-means, SVM, or neural network.
(a) Providing diﬀerent perspectives with diﬀerent algorithms.
i. Human-guided sampling - check the distribution - heat map forthe original data, scatter the sampling data over the heat map(b) Presenting the algorithm results in an interpretable and intuitive way.

5.2

A White-box Perspective:

1. How to provide a scrutable user feedback mechanism, i.e., provide user
with an understanding of how his/her input aﬀects the underlying algorithm/method.
(a) Infer user intent from user interactions or feedback: interactions with
raw data and transformed data (e.g., clusters)i. Single data item: eﬃcient (possible feedback grows linearly w.r.t.
#data items), but requires users to have a clear idea about user
feedback in advance. For example, users could assign the exact
label of a particular data item.

8

ii. Relative comparisons between data items: ineﬃcient (possible
feedback grows quadratically w.r.t. #data items), but it is moresuitable when users have only rough idea about their pairwise
decision or preferences. For example, users could only specify
the two data items should belong to the same cluster or diﬀerent
ones.
(b) Active learning from machine learning context could be utilized tolead users to answering the most critical question that is maximally
helpful to the system.
i. The main issue is how to provide intelligent user interfaces thatcan eﬃciently and eﬀectively collect the user inputs.
ii. Gamification could be one interesting way of enhancing user participation in the context of the active learning tasks.
(c) Transparent communications between a computer and a user: reveal
how a user’s feedbacks will influence the model and then guide theuser to provide further feedback in the right direction.
2. Human-guided feature selection(a) Examine the data from diﬀerent angles, e.g., Interaxis system foruser-driven axis forming via data selection of users’ preference(b) Interactive feature selection/engineering
3. User-steerable machine learning(a) Machine learning that can support various user feedback and serveuser intent. Traditionally, machine learning methods have incorporated user feedback in several classes of methods such as semisupervised learning and active learning. However, how the user intent
is incorporated into a machine learning model has been considered ina limited form. For instance, in the context of clustering, most of themethods assume user input is given as pairwise clustering constraints
such as cannot-link and must-links. However, user feedback can bein much more flexible and diverse forms, and thus machine learning models should be re-invented to better incorporate such humanneeds.
(b) Real-time responsive machine learning due to user feedback in ahighly interactive environment. The algorithm can be too fast for
user to respond, or on the other side too slow for human (e.g. because of the long running time). The challenge is to keep the user
and algorithm synchronised.
(c) Progressive visual analytics: 1) mark the stable results so that themodel will not process them later; 2) iteratively merge similar results
and modify the parameters for fast convergence.
4. Uncertainty

9(a) Data noise (Gaussian or shot), missing data, conflict, errors in data(some ML algorithms are sensitive to such errors). The challenge
is to visualise such data uncertainties to make the users (who runs
algorithms on the data) aware of them.
(b) Model approximate solution(c) Bi-directional function: smooth communication between the high dimensional space and low dimensional space. For example, if the user
find one pattern in the low dimensional space, he should check it in
the high dimensional space to verify this. If he changes something
in the low dimensional space, the high dimensional space should bechanged accordingly. Potential issue: how to handle the distortionin the low dimensional space.
5. Lack of ground truth given large-scale streaming data; sliding window
6. Challenges specific to Big Data: distributed/parallel processing of ML
algorithms, which may have implication for ‘white box’-related issues, as
users need to understand the details of how the algorithms run.
There should be much more challenges we have not covered from our discussion,
but at least we strongly believe this list should give a comprehensive guideline
about which challenges should be mainly tackled when trying to achieve a true
integration and machine leanring and visual analytics for big data analysis.

6Group 3: Visual Analytics of Complex Streaming Data

In the current era of continual data production, Volume is not the only BigData
challenge to address. Velocity and Variety are two key problems often faced
together when data is continuously streamed from various sources. More andmore streaming data sources are available and many tasks arise that require
Visual Analysis of this data in a timely manner. This is generally the casewhen real-time situational awareness is required such as in crisis management,or real-time intelligence analysis. Analysis methods, visualization techniques,
and interaction methods have to take into account the specific characteristics ofsuch sources.
What we consider here as streaming data is any type of information that iscontinuously produced and which needs to be read and captured once released– otherwise it would be quickly forgotten and left unreachable. Such examples
can be CCTV camera recordings, sensor readings, or also instant messages.
This type of Big Data challenges Visual Analytics in all its design stages.
The proper nature of the data with a continual modifications influences thedata analysis from its earliest stages. Changes of such steps might render thevisualization of past data invalid with respect to recently changed analysis goals.
Depending on how the system records and encodes past informations, this hasconsequences on what could and should be done especially when we bring inuser interaction. For example, changes in the data may be small or dramatic,
and both the analysis and views may be influenced either locally or globally when local changes may also trigger larger global changes.
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6.1

Visual Analytics Challenges

We have identified six domains where strong contributions can be brought toperfect stream data visual analysis:• Computational complexity and scalability: We need fast algorithms forreal time processing even for the visualization itself. We should think ofincremental methods, that could update analysis and visualization locallyand globally. Sampling data is imposed when facing huge streams. Usefulinvestigation leads: blue noise, reservoir sampling, computational models
from high performance, extreme-scale, and cache oblivious computing.
• Analytic complexity: Here we are interested in what analysis is relevant
to perform in real-time. And this is relative to the proper analytical task
dedicated to stream data. As for example, we need to fuse in real time
data sources so we can build comprehensive analysis from heterogeneousand multiple data. We should also take into account that data is often
integrated from diﬀerent time scales, comes at diﬀerent rates and speed,
and is visually analyzed on even diﬀerent time frames. This integrationand the continuous production of data often forces the system not to keep
all data as is, but to progressively integrate it and sometimes even forgetit – creating diﬀerent abstraction stages for a same piece of information.
How can we manage in this condition integrative analysis which builds
similarity and tries to derive causality – critical for situation awareness.
• Visual complexity: This is the step where perception and cognition forvisualisation come into play. What can we deliver in a static manner,
and what should be delivered in an animated manner? Many challenges
rise here because of the very dynamic nature of the data itself, and ourown brain’s tendency to focus all cognition on pre-attentively detected
dynamic changes.
• Interaction complexity: How can users interact with the streaming data?
We need new interaction paradigms. Traditional interaction tasks are notleft aside, such as search and query, but how to interact to emphasize on
facts and events? For example, in an animated visualization, we can intervene on the animation speed, to slow it down to relay important events,
although this may be specific to the application. How do we translate
the classical Visualization user interaction tasks taxonomy into streaming
manipulation?
• Evaluation complexity: Evaluation has always been diﬃcult to assess invisualization. How do we compare and evaluate a system in constantmotion? We need to figure out tasks and data out there so we can build
a test suite for contesters to compare diﬀerent approaches on the samedata with diﬀerent tasks in mind. A multilevel task taxonomy that iscomprehensive and descriptive of what a system intends to achieve, if not
a proper validation, makes a good baseline to understand and evaluate asystem.
• Domain/application complexity: There is wide panorama of domains thatprovide streaming data, from social media, to personal visual analytics and11crisis management. We need to understand what is needed from expert
users, and what is expected from non experts and the general public.
Should the Visual Analytics be in-situ, on mobile devices, or accessible in amixed manner? These constraints define all the analysis and visualization
steps, such as the computing power available or the visualization design.

6.2

Formal Definition of Streams

We propose the following general definition of streams to identify the potential
parameters which can influence visual analytics.
We will first consider streams as discrete content of information (such astransaction data). A stream σ will be defined as σ ⊆ L × A × T × S; for which
the quadruple (l, a, t, s) ∈ σ, with: l ∈ L the labels, and can be a unique label,a set of labels, or links in a subgraph; a ∈ A the value of the stream; t ∈ T thetime stamp of the stream; and s ∈ S the source emitting the stream item.
We can then define operations on streams, starting with start(σ) and next(σ)
to get the next quadruple from the streams.
When the set of labels L is too large we can partition it into interestingclasses L = L1 ∪ L2 ∪ . . . ∪ Ln and replace streams labels with labels of thecorresponding classes.
Now, we can also bring the focus on a given time window [t0 , t] t0 < t andstudy the characteristics of a collection of streams within this time window, suchas:
• the sum of stream
values:∑fΣ (l, s, t) = {a : (l, a, τ, s) ∈ σ ∧ τ ∈ [t0 , t]}• the maximum stream value:fM (l, s, t) = max{a : (l, a, τ, s) ∈ σ ∧ τ ∈ [t0 , t]}• the frequency of quadruples:c(l, s, t) = |{(l, a, τ, s) ∈ σ : τ ∈ [t0 , t]}|• the list of q most important labels with respect to characteristic f in thetime window: T op(q, f, s, t)• and other graph-based metrics, such as connectivity, (l1 , l2 )-reachability,
degrees, bipartiteness (fractional approach to link weights), . . .
With such framework in mind, we can operate analytically on streams, andvisualization operations may be translated into such operations. In the monitoring of streams we include also automatic “alarms” signaling critical or unexpected behaviour of the streams.

6.3

Stream VA framework

The main idea is to integrate an updater module that will come between data,
user, and the classical visual analytics framework. Given the multiple potential
source updates at diﬀerent points in time, and based on usersı́nput, the Updater
will manage with changes must be local, global, and which local changes cantrigger bigger global changes. The updater also needs to manage time, and thediﬀerent abstractions of the data (old data may be kept only as an average forexample). See Fig. 4.
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Figure 4: Stream VA framework

6.3.1

AnalysisAnalysis component include:• Detection of events: Monitoring for situational awareness• Data fusion, Cross-media analysis• Comparison with historical data (Min, max, etc)
• Searching for relevant information from data streams• Pattern identification, Pattern matching• Similarity/correlation/causality analysis: Bringing in heterogeneous data
to reveal new findings (from the crossing of information)• Data mining/text mining: topic modeling• Network analysis : Diﬀusion, Opinion leader• Clustering analysis: forming groups, spread, splitting groups
6.3.2

Visualization

Visualization component include:• Timeline: snapshot (history)• Main visualisation: integrated visualization• Multiple window: each source (for details on demand)• Sedimentation• Visual hierarchy• Animation (local update, global update)• Change of layout: subtle changes/radical changes
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6.4

References[1] Data Streams: Models and Algorithms, Charu Aggarwal (Ed.), Springer,
January 2007.
[2] Yuzuru Tanahashi, Chien-Hsin Hsueh, and Kwan-Liu Ma, An Eﬃcient Framework for Generating Storyline Visualizations from Streaming Data, IEEE Transactions on Visualization and Computer Graphics[3] Quan Hoang Nguyen, Peter Eades, Seok-Hee Hong: StreamEB: Stream Edge
Bundling. Graph Drawing 2012: 400-413[4] Milos Krstajic, Daniel A. Keim: Visualization of streaming data: Observing
change and context in information visualization techniques. BigData Conference
2013: 41-47[5] Huron, S., Vuillemot, R., Fekete, J. D. (2013). Visual sedimentation.Visualization
and Computer Graphics, IEEE Transactions on, 19(12), 2446-2455.
[6] Alsakran, J.; Yang Chen; Ye Zhao; Jing Yang; Dongning Luo, STREAMIT:
Dynamic visualization and interactive exploration of text streams, in Visualization Symposium (PacificVis), 2011 IEEE Pacific , vol., no., pp.131-138, 1-4
March 2011[7] Nan Cao; Yu-Ru Lin; Xiaohua Sun; Lazer, D.; Shixia Liu; Huamin Qu,
Whisper: Tracing the Spatiotemporal Process of Information Diﬀusion in RealTime, in Visualization and Computer Graphics, IEEE Transactions on , vol.18,
no.12, pp.2649-2658, Dec. 2012[8] Stolper, C. D., Perer, A., Gotz, D. (2014). Progressive visual analytics: Userdriven visual exploration of in-progress analytics. Visualization and Computer
Graphics, IEEE Transactions on, 20(12), 1653-1662.
[9] Bosch, H.; Thom, D.; Heimerl, F.; Puttmann, E.; Koch, S.; Kruger, R.;
Worner, M.; Ertl, T., ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering, in Visualization and Computer Graphics,
IEEE Transactions on , vol.19, no.12, pp.2022-2031, Dec. 2013

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List of Participants• Vladimir BATAGELJ , University of Ljubljana, Slovenia• Wei CHEN, Zhejiang University, China• Jaegul CHOO, Korea University, Korea• Peter EADES, University of Sydney, Australia• Seokhee HONG, University of Sydney, Australia• Yun JANG, Sejong University, Korea• Steﬀen KOCH, University of Stuttgart, Germany• Koji KOYAMADA, Kyoto University, Japan• Shixia LIU, Tshinghua University, China• Kwan-liu MA, UC Davis, USA• Hideki MIMA, Tokyo University, Japan14

• Hiroko MIYAMURA, Japan Atomic Energy Agency, Japan• Norihiro NAKAJIMA, Japan Atomic Energy Agency, Japan• Jorji NONAKA, RIKEN, Japan• Kenji ONO, RIKEN, Japan• Benjamin RENOUST, NII, Japan• Jinwook SEO, Seoul National University, Korea• Yingcai WU, Zhejiang University, China• Kai XU, Middlesex University, UK• Sung-eui YOON, KAIST, Korea• Xiaoru YUAN, Peking University, China

15