ANATOMY OF A VISUALIZATION ON-DEMAND SERVER

A Service Oriented Architecture to Visually Explore Large Data Collections

Romain Vuillemot, B

eatrice Rumpler and Jean-Marie Pinon

LIRIS, INSA Lyon, Universit

e de Lyon, F-69621, Lyon, France

Keywords:

Visualization On-Demand (VizOD), Information Visualization (InfoVis), Service Oriented Architecture

(SOA).

Abstract:

Facing the relentless information volume increase, users are not only lost in information overload, but also

among the various ways to depict it. In this paper, we tackle this issue by providing end-users access to up-

to-date visualizations techniques, using remote services coupled to their local interactive environment. The

outline of a Visualization On-Demand (VizOD) architecture is introduced, packaging information visualization

processes into services reachable over a network. Our goal is to provide end-users ﬂexible and personalized

visual overviews of large datasets. We implemented a prototype partially validating our architecture, and

discuss preliminary results of our experiments and give future work perspectives.

1 INTRODUCTION

In this paper we are interested in making visualiza-

tion techniques broadly available to end-users accord-

ing to his data, needs and task to achieve (see Fig-

ure 1). Availability is meant in terms of time-to-

product, skills required and span of choice to offer

users the right visualization technique, coupled into

their local interactive environment (software and in-

teractive devices), at the right time.

Figure 1: General user’s data, needs and tasks.

Painted with broad strokes, the major technical is-

sue with visualization techniques is the data format

heterogeneity and the lack of reliability: there is a

huge gap between a proof of concept issued from re-

search works and a out of the shelve tool. Advanced

contributions exist, but scattered in so many different

application ﬁelds. For instance, visual data mining

tools are very proliﬁc in biology (Adai et al., 2004)

with stunning results facing real life problems, espe-

cially dealing with data masses. New heuristics of

ﬁnding patterns in huge structures are available for

a speciﬁc tasks, but those inspiring data depiction re-

main domain speciﬁc. At the end of the day, end-users

cannot beneﬁt from most of the scientiﬁc tools even

if they look inspiring and potentially useful. And ﬁ-

nally, concerning interactive environments, the desk-

top metaphor is only still massively used because

of universal availability: innovation cannot make its

breakthrough.

As companies massively digitalize data for pro-

ductivity, ubiquity and quality management, users

would like to follow the behavior of massive and

complex data evolution, coming from many sources.

Whereas there exists tools to perform dedicated

analysis of the data, as far as we know there is no

way to get a visual overview carrying insights that

will trigger a more complex investigation with a dedi-

cated interface, regardless of the data or task (see Fig-

ure 2). In other words, there are no generic visual-

ization setups dealing with both accurate and global

information. Our goal is to uncover phenomena un-

Vuillemot R., Rumpler B. and Pinon J. (2008).

ANATOMY OF A VISUALIZATION ON-DEMAND SERVER - A Service Oriented Architecture to Visually Explore Large Data Collections.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - HCI, pages 86-93

DOI: 10.5220/0001715000860093

 SciTePress

seen at the ﬁrst sight, which will guide the user to a

speciﬁc data subset or data projection.

Existing solutions are either basics visualization

techniques such as sorted lists or unorganized items,

never surpassing ordinary tables or spreadsheets.

Thus, optimized access is only available when users

change context by switching execution environment.

But if no solutions are available then a whole prod-

uct life cycle is started, inducing cost and time waste.

This process also runs the risk of decreasing user’s at-

tention and productivity. New approaches have to be

considered to keep users in the same interactive en-

vironment, with the same interactive devices he mas-

ters, just with a switch of data being analyzed.

Figure 2: Generic overview (gray area) of data structures

leads to more speciﬁc analysis.

Actors needs are identiﬁed as follow:

• End-User Needs. There is a need for abstract

data handling solution to get an easy and quick

visual insight of the data, with appealing visual

metaphors. Such a process has to get rid of key-

board or not requiring any symbol being entered

into the system (in case user cannot formalize his

needs). Users have different background richness

and cultures, so individual accessibility character-

istic must be taken into account, such as visual

deﬁciency.

• Designer Needs. They are experts in the ﬁeld

of translating user needs into software or assem-

bly of software coupled with interactive devices.

Whereas design are stored in a guideline format

(Shneiderman and Plaisant, 2004), they lack for-

malization and evaluation. Re-use of existing li-

braries and environments becomes crucial with an

increasing systems complexity and heterogeneity.

The life cycle of products has to be etended as

well.

• Managers Needs. Responsibilities involve ad-

vanced monitoring tools with high reliability.

Trends are a way to anticipate the future and can

raise by means of complex interactions analysis or

long term data integration.

This paper is organized as follow. Section 2 fo-

cuses on the two major scientiﬁc ﬁelds being bridged,

that are Information Visualization and Service Ori-

ented Architecture. Section 3 focuses on the archi-

tecture outlines. Section 4 describes a prototype that

has been developed. Section 5 discusses results and

perspective. Section 6 concludes.

2 RELATED WORK

Our approach deals with Informations Visualization

(InfoVis) techniques, which technical lacks are to be

covered by web services packaging included in a Ser-

vice Oriented Architecture (SOA). A synthetic com-

parison is given on Table 1, and similar attempts are

also listed and analyzed.

2.1 Information Visualization

Conceptually, the fundamental goal of InfoVis is to

ﬁnd the right visualization at the right moment. A

complete visualization process results in outputs such

as maps to discover interrelationships between data.

Relationships can be either internal or external, help-

ing to understand complex static or dynamic dataset

growing over time or actions. Human capabilities are

thus enhanced, but limited cognitive memory must

be taken into account in order not to overload users

with information. The next step is for codes or user’s

knowledge to be integrated to reduce information di-

mension. Limited display space must also be con-

sidered, that can be solved by coordinated multiple

views, either from a static perspective which consists

of two or more distinct views supporting the investi-

gation of a single conceptual entity (Michelle et al.,

2000). Or either dynamically by synchronizing dis-

tinct views over time and/or user actions (Shneider-

man, 1996).

Technically, the underlying problem is that visu-

alization is hard coded to data and task to achieve.

Today, reusing a technique for other data means start-

ing another conﬁguration/implementation cycle ac-

cording to informal design guidelines. These recom-

mendations lack formalization and are given in pat-

tern format which has to be understood by experts,

inducing extra costs. Another limit is that visualiza-

tions are local to user’s application and dependent of

his computing power. And because contributions are

scattered in so many different application ﬁelds, there

ANATOMY OF A VISUALIZATION ON-DEMAND SERVER - A Service Oriented Architecture to Visually Explore

Large Data Collections

are neither central repository, nor evaluation. Some

lists exist, such as Many Eyes

or Visual Complex-

ity

. The latter consists in an updated screenshot and

video repository: but there are no semantic taxonomy

providing automatic access to visualization.

2.1.1 Data Transformation Models

Our goal is to understand the visualization process

in general, regardless data types, task or application

domain. Then a model oriented analysis of the vi-

sualization has to be performed (Butler et al., 1993).

This approach has been commonly used, resulting in

many models. We focus on two major models from

two distinct ﬁelds that are Information Visualization

(Chi, 2000) and Scientiﬁc Visualization (Haber and

McNabb, 1990). See Figure 3.

Figure 3: From left to right, (Chi, 2000) and (Haber and

McNabb, 1990) models.

Both models consider the visualization process as

a data ﬂow, conceptually cut into steps: they end up

with similar stages, but they are not complete enough

as they do not integrate interactions. We proposed a

slightly different model based on the previous ones,

but including interactions: we don’t consider the vi-

sualization process as a full data ﬂow but merely as

a sequence of operations being independently per-

formed by means of actions. Our model decomposes

the Information Visualization process into 3 stages,

each coupled with user actions (see Figure 4). In-

teractions can be either manual (user’s action), either

semi-manual (user’s action triggers a system reaction)

or automatic (system’s action).

The model description is as follow:

Extraction: is a step considering any perspective of

visualization such as semantic (RDF), physical

http://services.alphaworks.ibm.com/manyeyes/

http://www.visualcomplexity.com/

Figure 4: Holistic data transformation model including both

data transformation processes and interactions.

(matrix, directories) or even social, which extract

different points of view from the data.

Selection succeeds in reducing the dataset by se-

lecting (SQL, SPARQL), aggregating and project-

ing in an appropriate manner, freeing the user

from a potential overload.

Layout: is to give a spatial attribute to abstract data

structure such as graph, lists or multidimensional

sets. The layout can be made out of 2D maps but

can also be a 3D model.

Organization is an action that will change the lay-

out attribute of data.

Render: is to transform abstract data layout into im-

ages or 3D models.

Filtering means to post-process (using image

analysis techniques such as Gaussian blur or

Laplacian ﬁlter) to provide a pre-processed visual

result (Vuillemot and Peralta, 2008). These math-

ematical computations based on 2D-signal trans-

formations help, for instance, users to fade details

or highlight contours.

The cut in layers helps to identify and describe

each technique, that can now be seen as independent

modular programs.

2.2 Service Oriented Architectures

Service Oriented Architecture (SOA) is an architec-

ture style that aims at reorganizing business processes

into loosely coupled packages. The packages are dis-

tributed into modules reachable over a network. The

simplicity of use and universality of access makes it a

design style helping the reuse of self-describing com-

ponents. The components are interfaced as individual

ICEIS 2008 - International Conference on Enterprise Information Systems

entities with the propensity to build applications very

quickly. Services goal is to allow functionality to be

stuck together to form ad-hoc applications reusing ex-

isting software service. The capabilities of adaptabil-

ity and evolution are high by adding a new service,

reducing developing costs and a quick deployment.

Service are software reachable over a network

independently from the underlying implementation.

Languages describing them are:

• Interfaces are published in the WSDL ﬁle (XML-

Based Document that describes how to commu-

nicate with the Web Service) (Christensen et al.,

2001).

• Service repositories store WSDL ﬁles and are us-

ing UDDI (Universal Description, Discovery and

Integration) helping to match with user’s needs.

(UDDI, 2000)

• Clients having retrieved relevant interface will

contact the service provider using SOAP (SOAP,

2000).

Service composition (Agarwal et al., 2005) en-

ables resources to be merged and responds more

quickly and cost-effectively to changing market con-

ditions. Service helps not having license/software

distribution issues, and can reduce distribution costs,

piracy and reverse engineering.

2.3 Visualization Service

The challenge of generically beneﬁting from visu-

alization techniques has already been faced through

many researches carried out in various disciplines. As

far as we know, the closest and most proliﬁc ﬁeld

is Scientiﬁc Visualization, providing related architec-

tures. (Wood et al., 1996) introduced a client/server

communication based on a simple reference model to

perform data visualization. The visualization is done

over the web, and focuses only on a speciﬁc data

type which are plots. (Bonneau et al., 2005) intro-

duces a modular visualization environments enabling

users to change the data pipeline. A GUI interface

is described, where dynamic change of the visual-

ization pipeline can be done by users. Finally (Bla-

zona, 2007) is a Scientiﬁc Visualization system offer-

ing web services facilities, but which is not based on a

model. Thus visualization is seen as a whole process

which steps can’t be isolated.

Table 1: Characteristics of InfoVis and SOA.

Type/Field InfoVis SOA

Location Local Distributed

Coupling Tight Loose

Flexibility Bundle Package

Messages File format Messages

Communication None Protocol

3 A VISUALIZATION

ON-DEMAND ARCHITECTURE

The key idea of the Visualization On-Demand (Vi-

zOD) architecture is 1) to separate into indepen-

dent modules the visualization process according to

our model (as seen on Figure 4) and 2) distribute

processes, regardless the data being studied. This

modular approach has to be combined according to

a strategy, taking into account both technical con-

straints and users’ preferences.

Using on-demand paradigm means we consider

the rendered data stream as a media, such as movies

with Video On-Demand (VoD) that can be chosen ac-

cording to a user action. Conceptually, this expression

is well-ﬁtted since we consider the visualization as a

stream of existing knowledge, just with another per-

spective. Technically, it holds many identical prop-

erties as VoD, such as cache, replication and perfor-

mance.

3.1 Architecture

The aim is to make visualizations technique as black

boxes, with a focus on interfaces and communication

protocols.

3.1.1 Processes Subdivision

Subdividing means making smaller parts (called busi-

ness processes) of a larger system, operating inde-

pendently. Each business process has properties (de-

scribed in a UDDI repository) such as a description

including the task it achieves, performance and com-

plexity. These information will be useful to respect

users Quality of Service constraints.

While communication among the processes be-

comes asynchronous, modules keep interdependence

constraints. For instance a change in the layout will

trigger a new render. A color change in the graph de-

piction will leave an identical layout, but here again

render will have to be regenerated. We used as ex-

change protocol among the modules existing interme-

diate data transformation. For instance, the extraction

ANATOMY OF A VISUALIZATION ON-DEMAND SERVER - A Service Oriented Architecture to Visually Explore

Large Data Collections

module will communicate a graph structure to the lay-

out process as it would be done in a monolithic archi-

tecture. In other words, external interface mirror in-

ternal temporary data residing in computer memory.

The language choice turned out to be XML-like to

wrap messages as showed on Figure 5. Then an addi-

tional SOAP communication protocol layer is added.

Figure 5: Modules are interfaced with speciﬁc languages,

encapsulated in SOAP messages.

3.1.2 Processes Distribution

Business processes can be located at any places, and

reachable through a service repository. Process distri-

bution means that some processes may remain local

to users (e.g. because of privacy issues) while other

may be distributed and reachable other a network (e.g.

because they require lots of computing resources).

Figure 6: (a) strategy is to use remote layout process (b)

strategy is to keep local interactions only.

There exists many distribution strategies that can

be complex and dynamic (changing over time, ser-

vice availability or service load). Two examples are

described on Figure 6: (a) shows that user hosts the

dataset and transfer only data structure to perform a

layout process on it. This is a case where the layout

needs lots of computing power (b) shows an opposite

strategy, where user selects only the data and interacts

with the render only. This is a case where a dataset is

shared and reached through a local interactive envi-

ronment.

3.2 Strategy of Access

A strategy is a common way of combining small steps

to tackle a bigger problem. A step will be regarded as

a group of processes, which are selected and glued

together to solve a task. Assembly of steps will be

done as close as the way the mind is working and

will be called patterns. These patterns are following

common orchestration that have been subject of study,

such as Schneiderman’s Overview Zoom and Details-

On-Demand (OZD) (Shneiderman, 1996).

3.3 Personalization

While a company is an organization having the same

focus, individuals have speciﬁcities to be considered.

Even if every task or context may be different, there

exists a visual knowledge about data structures (e.g.

tree-like structures similar as Figure 8) that are com-

mon to every kind of information. Learning how to

understand and master this knowledge requires time,

but once done it can result in time savings by cutting

delays to visually master new datasets. Thus, user’s

visual habits and preferences have to be identiﬁed and

stored.

Personalization (Brusilovsky et al., 2007) is a way

of taking user’s preferences into account. Researches

have been carried out in such direction as in informa-

tion retrieval systems, in order to reduce datasets ac-

cording to user’s explicit or implicit preferences. Ex-

plicit preferences are user selections and conﬁgura-

tions operations, such as local environment choice or

service choice. Implicit preferences are user’s history

or any typical behavior registered in a non-intrusive

way. For instance, if a speciﬁc service or group of

services are invoked many times, they will be consid-

ered as a preference, even if no question has ever been

asked to the user (Eirinaki and Vazirgiannis, 2003).

Preferences can also vary from short to long term

interest. Short term preferences are edge colors, any

encoded knowledge (such as symbols) and ﬁlters of

the render which aims at solving a task in a speciﬁc

context. Middle term preference is the data layout

which can’t vary (whereas colors can) in order not

to disturb user’s mental model, which is his internal

vision of a virtual scene. Finally, a long term inter-

est concerns the interactive environment in which are

integrated visualizations.

Preferences will be stored in a User Visual Proﬁle

and will be available regardless user’s location.

ICEIS 2008 - International Conference on Enterprise Information Systems

4 PROTOTYPE

To validate our architecture, we developed a ﬁrst

VizOD prototype using existing visualization tech-

niques and interactive environments. Advanced tech-

nical details are available in (Vuillemot and Peralta,

2008). Our approach was to implement the strategy

described in Figure 6 (b) which Use Case is available

Figure 7.

Figure 7: Prototype Use Case.

The dataset is a movie database

. The data ex-

traction consists in performing queries, selecting 1)

users and 2) for each users their rated movies. That

selection is done by means of a web interface allow-

ing SQL-like queries. The extracted result consists

in a tree-like structure where the root is an artiﬁcial

node connecting all users with their rated movies as

leaves. The graph layout techniques used was origi-

nally aimed to display protein networks (Adai et al.,

2004). Other graph visualization tools exist such as

(Auber, 2003). The render step results in an image

with annotated data (containing details about movies),

provided in a separate ﬁle structured in a XML-like for-

mat, KML (Keyhole Markup Language). The result is

the picture displayed on Figure 8.

The image looks intriguing, but lacks of efﬁciency

since it holds only structural information: it has to be

included in a user’s interactive environment and be

more detailed.

We selected Google earth (GE) as an interactive

environment (with all geo-spatial features disabled).

A screenshot is available Figure 9. GE is installed and

run by the client, and connects to VizOD by mean of

HTTP requests. Using HTTP helps to keep away ﬁre-

wall issues or any complicated network conﬁguration.

VizOD will seamlessly map onto GE’s 3D sphere an

image according to user’s altitude and angle of view,

following (Shneiderman, 1996)’s recommendations.

http://www.imdb.com/

Figure 8: A tree-like layout visualization of a query result.

The result is a 3-layered multi-scale strategy com-

bining external business processes, and resulting (by

decreasing altitude) in an overview layer, zoom layer

and details layer. The overview layer aims at showing

global trends, then it will be connected to a blurring

render service, to remove details and raise trends. The

service interfaces Gimp

used in command line. The

zoom layer remains the original image. The detail

layer is the original image augmented with details on

top of it (included in the KML ﬁle). Bandwidth usage

has been minimized with that detail layer, by keep-

ing the zoom image locally and only requesting and

adding light KML data on top of it. The KML is con-

verted to additional vectorial graphics (lines, captions,

..) by GE.

It takes about 59s (on an ordinary server) to gen-

erate a single image. The layout process is the greed-

iest one, and generating or blurring images involves

nearly no extra time or resource use cost.

Figure 9: Image rendered mapped onto a 3D-sphere with

details on top of it.

Results, issued from experiments, was that GE is

a good metaphor since it implements a well-known

GNU Image Manipulation Program available at:

http://www.gimp.org/

ANATOMY OF A VISUALIZATION ON-DEMAND SERVER - A Service Oriented Architecture to Visually Explore

Large Data Collections

object that is earth. And users already used GE for

other purpose: then we managed to minimize the en-

vironment mastering phase by reusing an existing and

widespread tool. Usability was excellent since we re-

used a powerful environment, which remains very re-

active to every gestures and moves from users, even if

images were not fully loaded or updated yet. Thanks

to our VizOD approach, many visualization strate-

gies can be adopted, while the client stays focused

to a very same interface. The dataset can even to-

tally change without absolutely no interface change.

Adding new features on VizOD will be transparent

for users. Finally users appreciated to use an attrac-

tive means that is a 3D sphere (similar to the iPod

effect where the appealing wheel attracts users).

5 DISCUSSIONS

In this section we discuss applications of our architec-

ture.

Company beneﬁts issued from using a VizOD ar-

chitecture will come by outsourcing visualization to

experts, providing software as services with better

support and piracy prevention. The information sys-

tem rationalization will go further by centralizing

computing power at the same remote place and leav-

ing end-users with lightweight heterogeneous termi-

nals. The software maintenance routine is not on site

any more but on the VizOD servers, holding business

processes, which can be numerous allowing diversity

and backups alternatives. New economical models

will appear for producers.

Privacy and security issues are a big concern in

our approach. Regarding the steps of our model one

can see that rendered images prevent reverse engi-

neering process. Furthermore, extracting data struc-

tures only will give structural information and pre-

venting details being visible: quantity of information

is given, not quality. Finally, a service approach pre-

vents implementation details to be visible. However,

we keep SOA related issues, such as messages ex-

change that is prone to attacks.

User’s needs have to be considered globally, with

imperfections, such as short attention spans. Memo-

ries are also important aspects to deal with, especially

with data masses and in the case information is avail-

able in streams which are not stored: user’s full atten-

tion is then required. The focus can be on visualizing

updates, data growth, rather than content itself: the

change or the behavior becomes as important as the

instant content. The scalability and adaptability of the

VizOD architecture is crucial.

Services Mashup interfaces are new way for users

to compose services to build up and share new ones.

But is does not include yet extra process such as data

layout and render. A future work is to implement a Vi-

sualization Mashup Interface to cope with the lack of

semantic and integration visualization service repos-

itory. User needs tools in new era where web-users

have become actors using web interfaces.

New design process and product life cycle will

emerge. Programmers are not constrained, then they

will keep their own programming habits. A piecewise

conception process can be set up, with progressive

features. Other Research communities are addressed

such as cognitivists in order to observe users behav-

ior, interface designer to re-think the way to design

and evaluate. New actors such as artists can now fully

take part to the design process by including artistic

among one or many navigation step.

6 CONCLUSIONS

In this paper we introduced the outlines of a visu-

alization on-demand architecture (VizOD). We ﬁrst

proposed a holistic data transformation model, con-

sidering both visualizations and interactions. Every

step of the model are considered as business processes

that can either remain local or be distributed. Such an

approach allows end-users to beneﬁt and personalize

visualization services, and couple it into their local

interactive environments.

A present day result is a prototype resulting from

an assembly of existing tools and showing that even

non visualization-dedicated tools (such as graph ma-

nipulation libraries) can quickly result in an innovat-

ing application, following VizOD’s speciﬁcations, in

a context of affordable systems and non-expensive

softwares.

The missing masterpiece in our approach is a way

to encapsulate processes to make them automatically

discoverable and usable. There is also a semantic gap

between user’s task needs which are expressed in nat-

ural language and tasks the machine already knows

how to achieve. To tackle this issue, our next step is

building on line communities that will fertilize best

practices or novel uses. We will also focus on user

generated data (e.g. traces of use) that have to be

structured, ﬁltered and sorted. More generally, a sta-

ble on line framework has to emerge and be sustain-

able over time, and then lessons will be learn by wide-

spread use.

ICEIS 2008 - International Conference on Enterprise Information Systems

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