Digital Visual Exploration Library
Nicholas Tan Jerome and Andreas Kopmann
Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
Keywords:
Digital Library, Visual Exploration, Scientific Visualisation, Information Visualisation.
Abstract:
With the advancement of instrument precision, research facilities are generating data at an unprecedented
rate. These experimental results are stored in a digital library platform which the contents are later acces-
sible from within the facility or the public. However, the sheer volume of collected data is overwhelming
the capacity of researchers and impedes the process of browsing for the desired data. In this paper, we pre-
sent a concept of Digital Visual Exploration Library (DVEL) based on the confluence of two major research
domains—digital library and visualisation—that enables efficient browsing of the growing data within a di-
gital library. We complement the current state-of-the-art textual metadata description by integrating visual
exploration to address big complex data, i.e., data of large size, multimodal data and multivariate data. We
describe our concept based on use cases from three unique domains: climate research with Doppler wind lidar,
X-ray-imaging for entomology research, and medical imaging with ultrasound computer tomography.
1 INTRODUCTION
We are entering an era of data-intensive science where
significant data are produced by scientific experi-
ments worldwide. These data hold the key to verify
scientific hypothesis and having more data can help
drawing more reliable research conclusions. Hence,
numerous works focus on advancing the instrument
precision which allows researchers to produce data at
an unprecedented rate. For instance, the Large Ha-
dron Collider (LHC) particle accelerator can generate
60 terabytes of data per day (Brumfiel, 2011). With
LHC serving as an extreme case in the data-intensive
science, such large data are no stranger in most expe-
riments. Often, the data are stored in a digital library
which the contents are accessible from within the re-
search facility or from the public. By storing all the
information into the digital library, the sheer volume
of data complicates the data exploration process. The
main problem lies with users having little knowledge
of the large amounts of data.
To date, many scientific experiments are starting
to provide open data access to the public (Molloy,
2011) which at the same time enables citizen science
movement—citizens (non-scientists) taking part in
real-world experiments. There is a growing demand
to access scientific data from the public and impro-
ving the digital library framework is essential. To fa-
cilitate the exploration and analysis of a digital library
collection (Mathew et al., 2017), researchers emp-
hasise metadata analysis, i.e., word-frequency analy-
sis (Shubankar et al., 2011), co-occurrence word ana-
lysis (Isenberg et al., 2017), and probabilistic methods
like Latent Dirichlet Allocation (Griffiths and Stey-
vers, 2004). While these methods offer a generic so-
lution, but they fail to provide information related to a
particular domain (Mesbah et al., 2017). Moreover, if
the metadata is not complete or not informative, users
have no other options but to download a copy for off-
line verification which is cumbersome.
We propose instead to integrate visualisation
techniques into the framework of a digital library—
Digitial Visual Exploration Library (DVEL). In parti-
cular, we want to utilise visualisation to improve the
data browsing experience. With little knowledge of
the data, the visual presentation in the DVEL enables
users to explore the data faster and with higher con-
fidence. Since the user is the main actor throughout
the exploration process, the interactive interface of the
DVEL allows users to formulate hypothesis while ad-
justing their exploration goals (Keim, 2001). Keim
outlined the benefits of visualisation techniques for
browsing large data sets but has hitherto received little
attention in the digital library domain.
In this paper, we discuss design considerations to
provide appropriate visualisation interface as part of
the DVEL framework. We present three use cases that
illustrate the large data size, multimodal data and mul-
tivariate data. As digital library often provide its data
through a web portal, we are inherently limited by the
Jerome, N. and Kopmann, A.
Digital Visual Exploration Library.
DOI: 10.5220/0006650003410348
In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 3: IVAPP, pages
341-348
ISBN: 978-989-758-289-9
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
341
Table 1: The main components of the web portal for a list of digital libraries in the Physics domain (Last visited 21-07-2017).
Experiment Navigation Preview+Detail Retrieve
NASA Exoplanet Archive (Akeson et al., 2013) List Time Series tbl
Sloan Digital Sky Survey (Eisenstein et al., 2011) Guided search 2D Image jpg
Crystallography Open Database (Gra
ˇ
zulis et al., 2009) List Jmol (Hanson, 2010) cif
CERN Open Data Portal (South et al., 2011) List aod
Search
Archie_Nymph
Belbe_01
Belbe_02
Eucrib_01
Eucrib_02
Eucrib_03
Eucrib_04
ASTOR_04_2014
Title Label
LIBRARY
12:45 PM
Aranea
Archie_01
Archie_02
Archie_03
Archie_04
Download
Archie_01
Description
SELECT
PREVIEW
DETAIL
RETRIEVE
NAVIGATION
Archegozetes longisetosus is an oribatid mite (also
known as moss mites), reproduces asexually and has
defensive chemical secretions stored in huge opistho-
notal glands. Unlike other arachnids, oribatid mites
feed on particulate food like dead plant material (e.g.
decaying leaves), fungi and bacteria.
Figure 1: The main components in a web portal of the di-
gital library. Upon selecting a data set, a 3D preview of the
sample is shown together with its details.
bandwidth limitation. Hence, we perform data pro-
cessing based on the data state reference model (Chi,
2000). In particular, we prepare a compact represen-
tation of the data which is small in size and yet best
describe the data.
2 CONCEPT
The first use of the term digital library dated back in
1988 by (Kahn and Cerf, 1988), where they presented
an open architecture of a digital library system that
allows convenient access, either locally or remotely,
to geographically distributed users. Back then, most
information is not in digital form, and they described
their architecture as an extension to the traditional li-
brary which users must travel physically to the library
to utilise the resources (Kaur, 2015). However, such
community facility is losing interest from the public
in favour of digital technology advancement—a new
paradigm dubbed the digital library. With informa-
tion resources being digitalised, users are no longer
required to travel physically to a library; but instead,
they can access the information through any digitally
connected medium, i.e. web browser.
Numerous definitions attempt to define the digital
library (Cleveland, 1998; Lynch and Garcia-Molina,
1996; Seadle and Greifeneder, 2007; Trivedi, 2010),
which can be broadly distinguished either by its phy-
sical space and collections, by its digital environment,
or by its services. In this paper, we define library ac-
cording to its primary purpose—a facility which pro-
vides public access to its materials. The term digi-
tal refers to the digital technology which includes the
mobile phone, laptop, computer, network connection,
and cloud service. Hence, a digital library consists
of an extensive collection of digital information with
public access. Similarly, research facilities store their
experiment data in digital form which are accessible
from within the institution or the public.
With open data providing a web portal for remote
data access, users can search and download their de-
sired data conveniently. We identified the main com-
ponents in the web portal usage based on a set of col-
lective actions to find the desired data set: to search
(Navigation), to verify (Preview+Detail), and to do-
wnload (Retrieve). Figure 1 shows the main compo-
nents which are listed as below:
Navigation. A list and a search function to allow data
browsing.
Preview+Detail. A page view presenting details—
usually in tabular format—describing the selected
data set. Optionally, an interactive data preview is
present to emphasise essential features of the data set,
i.e., an interactive 3D visualisation showing the spa-
tial information.
Retrieve. The information of the data set can be
acquired or downloaded in various formats.
We studied four experiments in the physics dom-
ain and discovered that the corresponding web por-
tals mostly provide the Navigation and Retrieve com-
ponents (Table 1). However, the Preview+Detail is
not a prerequisite component and still mainly limi-
ted to textual descriptions. Instead, we highlight the
unique features of each data set by integrating inte-
ractive visualisation techniques such as 3D visualisa-
tion (Levoy, 1988) or information visualisation (Buja
et al., 1991). Although most information can be des-
cribed in texts but conveying complex information so-
lely with texts can be a daunting task. For instance,
an interactive 3D visualisation would provide better
spatial information. Taking the Crystallography Open
Database as an example, the molecular structure is
best described by an interactive 3D visualisation—
Jmol (Hanson, 2010).
To support better data exploration, we propose to
integrate the visual exploration technique as part of
IVAPP 2018 - International Conference on Information Visualization Theory and Applications
342
Table 2: The data stages of each use cases following the data state reference model (Chi, 2000). The Value stage represents
the raw data. The Analytic shows the post-processed data state. The Visual stage depicts the input formats required for the
final visualisation (View).
Use case Value Analytic Visual View
NOVA (Sec. 4.1) Image Aggregated Slicemaps Interactive 3D
Database Database
KITcube (Sec. 4.2) Relational Event Summary Encoded 2D Heatmap,
Database Database Images Plan Position Indicator (PPI),
Range Height Indicator (RHI)
USCT (Sec. 4.3) Image Aggregated Encoded 3D Fusion
Database Database Slicemaps
Value
Analytical
Abstraction
Visualisation
Abstraction
View
Data
Transformation
Visualisation
Transformation
Visual Mapping
Transformation
Figure 2: The data state reference model by (Chi, 2000). The black box denotes the state of the data, whereas the arrow shows
the action performed from one state to another.
the Preview+Detail component within the DVEL fra-
mework. The DVEL can provide more information to
complement any shortcomings from the textual meta-
data descriptions. Tominski described the visual ex-
ploration as an undirected search for relevant informa-
tion within the data (Tominski, 2006). Tominski emp-
hasised visual exploration as the process to provide an
overview of the data, to browse through subsets of the
data, and to interact with the data intensively. These
requirements define what functions a useful visual ex-
ploration tool should have.
Within the DVEL framework, the visualisation
component (Preview+Detail) acts as an add-on fe-
ature to enhance existing solutions. In particular,
the viewing component strives to better inform users
about the data without suffering from the scale of the
massive data.
3 DESIGN STRATEGY
Our approach to design the DVEL revolves around
two main considerations: technology limitations (data
preprocessing) and the effectiveness of data explora-
tion visualisation (three-step process). The techno-
logy limitations refer to the limited network band-
width and the diverse client requirements.
The final visualisation should be served at an in-
teractive rate. Hence, data transferred has to be small
and yet still able to represent the original data. Mo-
reover, the transferred data should match the diverse
client requirements, i.e., mobile phone, laptop or de-
sktop. To this end, we perform a data processing stage
which follows the data state reference model (Chi,
2000) (Figure 2). We decided on the type of final
visualisation and prepared the required input data ac-
cordingly. We reduce the data for a better network
bandwidth (data reduction) and prepare a hierarchy
of data with different data granularity to serve various
client requirements. Table 2 shows the data stages of
each use cases. The Value stage shows the raw data
input which can be an image database or a relational
database. Then, we perform data processing methods
on the raw data such as data reduction and data fusion.
Depending on the type of final visualisation, we trans-
form the data into the required visual formats such as
slicemaps (Tan Jerome et al., 2017b) or encoded ima-
ges (Tan Jerome et al., 2017a).
To design the visual interface in the Pre-
view+Detail component, we involve the domain ex-
perts in formulating primary features of the data
through the human capabilities (Binnig et al., 2016).
This approach allows the visual element to target the
primary users’ needs better. We follow the “informa-
tion seeking mantra” (Shneiderman, 1996) which ser-
ves as our design guidelines—three-step process: (1)
overview first, (2) zoom and filter, and (3) details on
demand. In the overview step, we identify interesting
patterns which best describe the data and enable users
to drill down the details of the data.
4 APPLICATION SCENARIOS
In this section, we discuss the design strategies—data
preprocessing and the three-step process—and appro-
priate visualisation techniques that fit into the Pre-
view+Detail component in three different scenarios:
Digital Visual Exploration Library
343
(1) visualising data of large size (gigabytes of data),
(2) merging multimodal data, and (3) summarising
multivariate data into a single representation.
4.1 Visualising Large Data
The NOVA
1
project (Schmelzle et al., 2017) serves
as an ideal use case where hundreds of small ani-
mals such as insects and other arthropods are scanned
using the synchrotron-based X-ray microtomography
(SRµCT) imaging technique. These tiny animals are
digitised and turned into large data sets with each size
ranging from 30 GB to 120 GB. The data are stored in
multiple collections of stack images where each stack
of images represents volumetric data. The primary
challenge lies in the size of the data, where transfer-
ring gigabytes of data through the network introduces
high data latency.
Data Preprocessing. To overcome the network li-
mitation, we reduce the data size but at the same
time preserving the quality of the data. As we are
limited by the network bandwidth, we adopted the
multi-resolution slicemap approach to serving vari-
ous clients (Tan Jerome et al., 2017b). The slicemap
is a 3D data structure in the form of a mosaic-format
image, which comprises a series of cross-section ima-
ges. For better user experience, we first load a low-
resolution slicemap (128
3
voxels) for instant visua-
lisation and followed by a high-resolution slicemap
(256
3
/ 512
3
voxels) loading in the background (Fi-
gure 4: Tier I). However still, the multi-resolution sli-
cemaps are reduced data which the quality is not com-
parable to the image rendered directly from the origi-
nal image. To address this drawback, we utilise the
resource of the High-Performance Computing (HPC)
server to stream images which are rendered directly
from the original data (Figure 4: Tier II).
Three-step Process. In this application, biologists
would like to identify the type of small animals wit-
hout suffering from the high data latency. We present
two approaches: thumbnail preview (additive blen-
ding) and the two-tier approach—surface rendering
on demand. The first approach is integrated into the
Navigation component, where we provide a list of
thumbnail previews (Figure 3A). The thumbnail pre-
view is a 3D image that enables biologists to accu-
rately identify the specimen. Since the image pla-
ceholder in the data list is small, we use primitive
techniques for fast render time by downscaling the
data and performing additive blending. To inspect the
data further, we provide an interactive 3D visualisa-
1
NOVA stands for Network for Online Visualization and
synergistic Analysis of tomographic data
tion with quality comparable to the desktop applica-
tion (Figure 3B).
4.2 Visualising Multivariate Data
In a large-scale experiment, not only the increasing
data size is a concern, but also the complexity of the
data. These data usually are multivariate data con-
sisting of different attributes such as temperature and
humidity (Kehrer and Hauser, 2013). Despite the
data being highly heterogeneous, researchers must,
however, combine these data with various attributes
and formats to formulate and verify their hypothe-
ses. Using the Doppler wind lidar data from the
KITcube mobile observation platform (Kalthoff et al.,
2013), we demonstrate the DVEL which allows mete-
orologists to browse through multivariate daily data
using the Doppler wind lidar interactive visualisa-
tion (Tan Jerome et al., 2017a).
Data Preprocessing. We separate the multivariate
data based on three different measurement types into
the Event Summary Database. Each type consists
of further three attributes, in which we then norma-
lise them with a constant interval, i.e., one second.
We then reduce the size and merge the attributes into
an encoded image (Figure 5). Although the encoded
images are in the 2D format, the type of final visuali-
sation determines how the 2D image is mapped, i.e.,
PPI plot maps the image into a circular scheme, and
RHI plot maps the image into a hemisphere plot. The
mapping is performed interactively using the GPU re-
source through the WebGL shader.
Three-step Process. The main objective is to provide
meteorologists with an overview of the daily trend,
in which we integrate three plots to study the wind
velocity: Plan Position Indicator (PPI) scans, Range
Height Indicator (RHI) scans, and Vertical Stare. In
contrast to the previous list view, we design an ag-
gregated view where we project daily information on
the screen display (Figure 6). In the visual analytics,
we use the daily vertical stare plot as the overview
to detect interesting patterns. We also provide hourly
view which shows a more detailed vertical stare, RHI
and PPI plots. By choosing a particular date (Naviga-
tion component), meteorologists can analyse the daily
overview interactively utilising the brushing techni-
que (Spence, 2001) on multiple attributes.
4.3 Visualising Multimodal Data
When multiple sources are present, we describe the
multivariate data as multimodal data. In the me-
dical domain, multimodal data stem from different
imaging techniques such as CT, MRI, or ultrasound
IVAPP 2018 - International Conference on Information Visualization Theory and Applications
344
A
B
Figure 3: Three examples of the arthropods in the NOVA project rendered using (A) the additive blending and (B) the surface
rendering approaches: the oribatid mite Archegozetes longisetosus (left column), the box mite Euphthiracarus reticulatus
(middle column) and an unidentified Diptera species fly (right column).
Server
Data Manager
SLICEMAP
Cache Selector
Image Rendering
GPU GPU
GPU
3D Data/
2D Slices Stack
Client Hardware
Requirement
Rendered
Image
Client Viewport
Configuration
Aggregated
Raw / Slicemap
TIER I
TIER II
Navigation Preview+Detail Retrieve
Additive Blending
images
Figure 4: The data preparation of the original data for final
visualisation at the client side. A set of preview images
rendered using the additive blending approach is provided
to the Navigation component. For the Preview+Detail, a set
of high quality rendered images are streamed to the client.
data (Kehrer and Hauser, 2013).
Server
Multivariate
Data
Data Types /
Viewport Size
Navigation Preview+Detail Retrieve
Vertical Stare
PPI
RHI
Encoded Images
Figure 5: The data preparation of the multivariate data
for final visualisation at the client side (Tan Jerome et al.,
2017a). The multivariate data are processed at the server
before serving to the clients.
Using the USCT
2
project as our use case (Gemmeke
et al., 2017), we have a digital library filled with mul-
timodal ultrasound data. The USCT project aims to
develop new image methodology for early breast can-
cer detection. The USCT detector produces three dif-
ferent types of images, which are the reflection image,
the sound speed image and the attenuation image. The
sound speed and attenuation images allow doctors or
scientists to classify lesions precisely, whereas the re-
flection image contains the information of the breast
2
USCT stands for 3D Ultrasound Computer Tomo-
graphy.
Digital Visual Exploration Library
345
JULY
28-07-2016
AUGUST
29-07-2016
30-07-2016
31-07-2016
01-08-2016
02-08-2016
03-08-2016
04-08-2016
NAVIGATION
Download
28-07-2016
SELECT
PREVIEW
+ DETAIL
RETRIEVE
DOWNLOAD
a
b
c
d
0 120
Range Gates
Time (hour)
Time (hour)
Distance (Km)
PPI RHI
Daily Vertical Stare
Hourly Switch
Figure 6: An aggregated view of the Doppler wind lidar
data where each item on the Navigation component shows
a daily overview of the wind velocity which consists of ver-
tical stare, RHI and PPI plots.
Server
Data Manager
SLICEMAP
RAW
Cache Selector
Multimodal
Data
Client Hardware
Requirement
Aggregated
Raw / Slicemap
Navigation Preview+Detail Retrieve
Data Fusion
Sound Speed
Attenuation
Reflection
Figure 7: The main components in a web portal of the di-
gital library. Upon selecting a data set, a 3D preview of the
sample is shown together with its details.
structure (Hopp et al., 2015).
Data Preprocessing. Similar to the data processing
in Section 4.1, we perform data reduction and data
transformation on the original data to produce a se-
ries of slicemaps. Also, we encode the three imaging
modalities into the slicemap which results in an enco-
ded slicemaps (data fusion).
Three-step Process. The goal of this application is
to enable doctors to identify possible tumour regions.
Rather than studying each modality separately, we
fuse the multimodal data and render the final image
using the normal fusion approach (Stokking et al.,
2001). The multimodal data are fused according to
the Ranger’s fusion scheme (Ranger et al., 2012). In
Figure 8 shows the multimodal view of a data set,
where:
Cancer (orange): Sound image thres-
holded at (1.52 ± 0.03) ×10
3
kms
1
and attenuation image thresholded at
(0.16 ± 0.04) × 10
3
dBcm
1
.
Fibroadenoma (green): Sound image threshol-
ded at (1.52 ± 0.03) × 10
3
kms
1
and attenu-
ation image not in the thresholded region of
(0.16 ± 0.04) × 10
3
dBcm
1
.
Fibrous bands or architecture (grey): The whole
reflection image range.
DATA
P10
P11
P12
P13
NAVIGATION
Download
P10
SELECT
PREVIEW
+ DETAIL
RETRIEVE
DOWNLOAD
P14
P15
P16
P17
P18
P19
Figure 8: The DVEL for the multimodal USCT breast da-
taset. The orange region shows the area with high sound
speed values suggesting possible tumour tissues. The green
region is the auxiliary attribute (attenuation) to support the
deduction of the tumour possibility.
We transform the fused dataset into 3D formats and
perform surface rendering to obtain an illustration de-
picting the possible tumour region.
5 DISCUSSION AND
CONCLUSIONS
The continuously growing data in a digital library will
pose a challenge in data exploration. Thus, supports
to enable users finding their data from the exhaustive
list are increasingly important. We proposed Data Vi-
sual Exploration Library (DVEL) that integrates vi-
sual exploration techniques as part of the digital li-
brary framework, where the visual display can repre-
sent the prominent feature of the data better (Keim,
2001). We presented three different use cases and
showed the relevance of our concept with the frame-
work of the digital library.
Due to the bandwidth limitation, we applied a per-
sonalised processing which reduces the memory foot-
print and yet retains the identity of the data. Also,
the smaller data enable a more responsive visual inte-
raction on the client. Furthermore, we utilise the GPU
resource by offloading graphics and data rendering at
the client. Although image rendered from the origi-
nal large data shows much more detail, the purpose
of our concept is to allow fast data exploration and
ultimately enable users to narrow down their search.
From our experience, the DVEL concept introdu-
ces another type of data to be stored—a compact vi-
sual data. Rather than storing only the original data,
we need to define a compact visual data which can be
either made of a single attribute or a collection of attri-
butes. The compact visual data has to be designed by
domain experts for better visual communication. The
preparation of the visual data requires close collabo-
ration among researchers of different domains which
might be cumbersome.
Although our use cases differ in its visualisation
purpose, we can set up a design strategy base on the
IVAPP 2018 - International Conference on Information Visualization Theory and Applications
346
data state reference model (Chi, 2000) and the three-
step process (Shneiderman, 1996). However, the cur-
rent state of the design strategy relies heavily on em-
pirical approaches which are not scalable. To this end,
designing a software tool that encompasses the design
strategy proposed can be attractive.
As the implementation of the DVEL in the three
use cases—NOVA (Schmelzle et al., 2017), KIT-
cube (Kalthoff et al., 2013), USCT (Gemmeke et al.,
2017)—are still at its early stage, user feedbacks from
the DVEL remains as an interesting component to
shape the future research challenges. In this paper,
we described mainly on the concept and design of the
DVEL but not the relationship between the meta data
and the visual components.
ACKNOWLEDGEMENT
We would like to thank the German Federal Ministry
of Education and Research for funding of ASTOR and
NOVA (grants 05K13VK5 and 05K16VKB, respecti-
vely). We also express our gratitude to Sebastian
Schmelzle, Michael Heethof, Thomas van de Kamp,
Torsten Hopp, and Michael Zapf for their helpful in-
sights.
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