Open Data Integration

Visualization as an Asset

Paulo Carvalho

1,2

, Patrik Hitzelberger

, Beno

ıt Otjacques

, Fatma Bouali

and Gilles Venturini

Gabriel Lippmann Public Research Center, 41 rue du Brill, L-4422 Belvaux, Luxembourg

University Franc¸ois Rabelais of Tours, Tours, France

Keywords:

Data Integration, Information Visualization, Open Data, Linked Open Data.

Abstract:

For several years, and even decades, data integration has been a major problem in computer sciences. When it

becomes necessary to process information from different data sources, several problems may appear, making

the process of integration more difﬁcult. Nowadays, more and more information is being sent and received

and is made available on the Web and Data Integration is becoming even more important. This is especially

the case in the emerging trend of Open Data (OD). Integrating data from public entities can be a difﬁcult

process. Large quantities of datasets are made available. However, an important level of heterogeneity may

also exist: Datasets exist in different formats, forms and shapes. While it is important to be able to access this

information, it would also be completely useless if we were not able to interpret it. Information Visualization

may be an important tool to help the OD integration process. This paper presents problems and barriers which

can be encountered in the data integration process, and, more speciﬁcally, in the OD integration process. The

paper also describes how Information Visualization can be used to facilitate the integration of OD and make

the procedure more effective, friendlier, and faster.

1 INTRODUCTION

The main aims of Data Integration are to se-

lect and combine information from different data

sources/systems/entities into a uniﬁed view, in a way

that users can exploit and analyse it conveniently. For

several years, it has been a major subject of study in

computer science (Ziegler and Dittrich, 2004). The

topic has recently gained new importance due to the

appearance of numerous new information sources,

like Social Media, Blogs, Scientiﬁc Data, commercial

data, Big Data and Open Data (OD).

These data sources increase the data volumes and

the potential number of providers signiﬁcantly, with

data data coming from public and private entities, as

well as from individuals. The relatively recent con-

cept of OD is a major example of this phenomenon.

OD makes information formerly hidden ”inside” pub-

lic and private organizations available and accessible

to everyone at little or no cost and without permis-

sion limitations. In order to beneﬁt from the presump-

tive high potential business-value of OD, data must

be made usable, meaningful and exploitable to permit

its integration (Davies, 2010). This paper addresses

this problem, discussing the main problems related to

Data Integration with a special emphasis on the dif-

ﬁculties directly linked to the Integration of OD. In-

formation Visualization – also known as InfoVis – is

presented as a core and powerful approach for back-

ing the integration process.

2 OD INTEGRATION

2.1 General Overview

The appearance of new information sources not only

contributes to the growing amount of information, it

also increases the heterogeneity of data sources. Data

Integration processes have become even more com-

plicated and demanding. OD integration is currently

a subject of major importance. As an example, it is

in the focus of the current EU research framework

programme Horizon 2020 (Commission, 2014). One

topic of the ICT2014-1 call (EuropeanCommission,

2013) is ICT-15-2014: ”Big Data and Open Data In-

novation and take-up”. It focuses on the entire value

chains and reuse of Open (and Big) Data. It is a

major problem because of the difﬁculty of integrat-

Carvalho P., Hitzelberger P., Otjacques B., Bouali F. and Venturini G..

Open Data Integration - Visualization as an Asset.

DOI: 10.5220/0005000600410047

In Proceedings of 3rd International Conference on Data Management Technologies and Applications (DATA-2014), pages 41-47

ISBN: 978-989-758-035-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

ing heterogeneous datasets. Datasets may be built us-

ing completely different methods (formats, schema,

metadata, etc.) (Rivero et al., 2012).

Today, organizations already integrate their inter-

nal data, using e.g. central repositories, data ware-

houses, or more process-oriented approaches like

service-oriented architectures for their operational

systems.

Figure 1: Public and Private Data Integration.

The integration of external data into these existing

IT landscapes is difﬁcult. An internal integration oc-

curs ”inside” an organization. The probability that an

organization can control the format rules, policies and

standards is higher than it is for external, autonomous

data sources. In the following sections, we discuss

OD ”external integration” problems and issues, and

the current status and solutions.

2.2 An Overview of the Current State

Many public organizations, from local to national and

trans-national levels, have already made their data ac-

cessible on the Web. Several initiatives and directives

inﬂuence these events: European Union PSI Directive

(EuropeanParliament, 2003), Freedom of Informa-

tion (FOI) initiatives in different countries and con-

tinents (UnitedStateDepartmentOfState, 2010) (Gov-

ernmentOfSouthAustralia, 2003), Canada’s Action

Plan on Open Government (GovernmentOfCanada,

2011), etc. One argument for publishing public data is

the fact that it has been paid by the citizens in general

(Vander Sande et al., 2012). So, over the last couple

of years, the public sector has already created Open

Government Data portals to open and share its data.

These data portals or catalogues offer a single location

where governmental data can be found (Maali et al.,

2010). Others are currently in development.

2.3 Problems and Challenges

The OD movement not only has beneﬁts. The fact

that public and private entities provide their datasets

brings issues of privacy, ownership, availability, us-

ability, accuracy and data combination (Janssen et al.,

2012). Different challenges related to interoperabil-

ity remain unresolved. Entities continue to build and

furnish datasets without applying common standards

and using heterogeneous systems. These datasets may

be constructed using different and inconsistent tech-

niques. Actually, and in general, Open Government

Data initiatives publish their data using one of the

following two general approaches (Kalampokis et al.,

2011):

• The data is available on the Web as downloadable

ﬁles in different formats, e.g. Excel, CSV, XML,

etc.

• The data is available on the Web using RESTful

APIs and SPARQL interfaces, as linked data.

Individual datasets, made up of data - and meta-

data in the best case scenario - are interesting and

useful on their own. Nevertheless, the positive and

collaborative effect of using public and private infor-

mation may be higher if data of different types (sci-

entiﬁc, social media, etc.) and delivered from several

entities is combined, compared and linked. Some of

the major problems and constraints which may be en-

countered when trying to integrate multi-source data

are related to the following topics:

• Structure and formats used - Given the high

number of different sources and datasets, it is

not astonishing that Public Sector Information

(PSI) is published following different modelling

paradigms (e.g. tabular, relational) (McCusker

et al., 2012) and formats: ZIP, CSV, XML, EX-

CEL, PDF, etc. Sometimes, data is even provided

in non-machine-readable and/or proprietary for-

mats;

• Metadata - Metadata is of paramount importance

for data integration and is one of the chief com-

ponents of PSI systems for OD provisioning. A

metadata schema is one of the main parts of a

PSI system which should be characterized in a

uniﬁed way (Bountouri et al., 2009). In other

words, metadata may be deﬁned as necessary and

adequate so that data can be understood, shared

and reused (Edwards et al., 2011). If metadata

provided with a given dataset is not well-formed

DATA2014-3rdInternationalConferenceonDataManagementTechnologiesandApplications

and/or complete, ﬁnal users may have difﬁcul-

ties ﬁnding its related dataset (Houssos et al.,

2012). Metadata provides the means to discover

datasets, access them and understand them. Meta-

data normally refers to information about con-

text and content (for example, a title, a descrip-

tion, an author, etc.) of datasets. Most of meta-

data schemas implemented in the public sector

have been designed for national requirements. In

Australia, for example, the AGLS Metadata Stan-

dard was adopted (NationalArchivesofAustralia,

2010), New Zealand adopted the New Zealand

Government Locator Service (NZGLS) while the

United Kingdom chose another option, the e-

Government Metadata Standard (eGMS) (Charal-

abidis et al., 2009);

• Accessibility, Permanence and Timeliness - If OD

is commercially exploited, the providers should

respond to typical business requirements in terms

of accessibility, permanence and timeliness. Out-

dated information, missing information, or infor-

mation that is not accessible because of techni-

cal or other issues, cannot be the basis for reli-

able business processes. On the other hand, it

seems unrealistic to hope that the public sector

with its limited resources can offer the same ser-

vice levels as commercial data providers. The

integration processes must tackle these problems

– or at least make them visible when they occur

(Gurstein, 2011);

• Trust and Data Provenance - More and more, the

need for having information and knowledge about

data provenance is important. Data provenance, if

it can be determined, may be used by users/data

consumers to evaluate and interpret the informa-

tion provided (Moreau et al., 2008). OD Integra-

tion processes and applications should be aware

of data provenance, and offer efﬁcient and reliable

ways to visualize and judge it;

• Multilingualism and cultural differences - The ex-

ample of the European Union, with its 28 mem-

ber states and a total of 24 ofﬁcial languages,

shows that the wealth of data that has been de-

scribed above is actually a linguistic mess. Fur-

thermore, cultural differences can already lead

to different semantics for basic integration prob-

lems: An address in France is not necessarily the

same “concept” as it is in Germany, for example.

Ideally, information represented in different lan-

guages should not hinder its integration. (Rehm

and Uszkoreit, 2011).

Even in a scenario where OD integration is tech-

nically possible, organizational and legal barriers may

exclude or complicate collaboration and the sharing

of data. Public and private entities may have some

constraints in opening and sharing their information.

They may claim ownership and/or control over certain

datasets (Martin et al., 2013).

2.4 Current OD Integration Solutions

Interoperability and standards are important to pro-

vide a solution able to analyse and process datasets

from various data sources, using different technolo-

gies and methods. Several solutions and systems, able

to help and support developers in processing a com-

plete and uniﬁed OD integration, have started to ap-

pear (e.g. Linked Open Data, CKAN, DKAN). They

are presented below.

2.4.1 Linked Open Data

OD may be deﬁned in different forms - it may be rep-

resented as Linked Data. Linked Data refers to best

practices for publishing and connecting data on the

web that are machine-readable and may come from

different sources (Bizer et al., 2009). The adoption

of these practices leads to a concept where there is a

global web space in which both documents and data

- from different and multiple domains - are linked.

When OD is Linked Data, it is called Linked Open

Data. The main objective of Linked Open Data is

to help the Web of Data to identify datasets that are

available under open licences (OD sets), convert them

to a Resource Description Framework (RDF) apply-

ing Linked Data principles and ﬁnally publish them

on the Internet. Furthermore, as well as Linked Open

Data being concerned with the data publication as-

pect, it also takes the data consumption perspective

(Bauer and Kaltenb

ock, 2011). Linked Open Data has

more advantages and less limitations and constraints

than OD. Currently, the so-called Linked Open Data

cloud already provides access to information covering

a large set of domains like economy, media, govern-

ment, life sciences, etc. The value and potential of

using all available data is huge.

In addition, while the idea behind OD is built on

the concept of a social web, the notion of Linked Data

is based on the semantic web approach (Bauer and

Kaltenb

ock, 2011) - a movement which promotes the

use of common standards on the Web, encourages the

inclusion of semantic data in web pages and allows

data to be shared and reused by any kind of appli-

cation in a cost-efﬁcient way. Sir Tim Berners-Lee

created a ﬁve-star model which describes the differ-

ent categories going from OD to Linked Open Data

ochtl and Reichst

adter, 2011), to help and encour-

age entities to link their data:

OpenDataIntegration-VisualizationasanAsset

Table 1: Sir Tim Berners-Lee ﬁve stars model.

Information is available on the Web

under an open licence and in any

format.

(*) + Same as (*) + as structured

data.

***

(**) + Same as (**) + only

non-proprietary formats are used

(e.g. CSV instead of XLS).

****

(***) + Same as (***) + use of URI

(Uniform Resource Identiﬁer)

identiﬁcation - people can point to

individual data.

*****

(****) + Same as (****) + data is

linked to other data so context is

preserved - Interlinking between

data.

In the ﬁeld of data management, Linked Open

Data is gaining importance. Several Open Govern-

ment Data portals, in various sectors and areas, are

already using Linked Open Data principles in their

systems (e.g. the Government Linked Data (GLD)

Working Group(W3c, 2014); the Linking Open Gov-

ernment Data (LOGD) Project(Twc, 2014); the LOD2

project(Lod2, 2014)).

2.4.2 CKAN

Comprehensive Knowledge Archive Network

(CKAN) is another project related to the OD integra-

tion topic (OpenKnowledgeFoundation, 2014). It is a

web-based Open Source data portal platform for data

management that provides necessary tools to the pub-

lic sector, other organizations and companies wanting

to publish and open their data. CKAN provides an

extensive support for Linked Data and RDF. CKAN

is already used by some important data catalogues

worldwide (e.g. the ofﬁcial Open Data portal of the

UK Government (Data.gov.uk, 2014); the prototype

of a pan-European data catalogue (Publicdata, 2014);

and Berlin’s Open Data Catalogue (Berlin.de, 2014)).

2.4.3 DKAN

DKAN is a Drupal-based

Open Data platform with

a full suite of cataloguing, publishing and visualiza-

tion features that help and support governments, non-

proﬁts organizations and universities in easily pub-

lishing data to the public (Drupal, 2011). Most of

the core open data features that exist on CKAN are

replicated in DKAN.

Drupal is a Content Management System which has

grown in popularity in the last few years due to its open-

ness, modularity and features (Corlosquet et al., 2009)

3 INFORMATION

VISUALIZATION AS AN ASSET

Information Visualization can be extremely helpful

when large amounts of data are involved. In many

scenarios, end users do not have the technical expe-

rience and knowledge to understand the meaning of

data and how to formulate queries for the desired re-

sults. They should nevertheless be capable to discover

how to link data and how data is enabled to build

queries which yield the expected results (Fox and

Hendler, 2011). Information Visualization could be

a major asset to help and support end-users on these

tasks. Many problems and difﬁculties on interpret-

ing, ﬁltering and viewing information can be avoided,

minimized and/or eliminated by using Information

Visualization. A Visualization System may be seen as

a block which receives data as input and interacts with

other entities to produce a graphical representation

of the received information (Duke et al., 2005). The

strength and power of Information Visualization is the

ability to present information in many and different

forms, graphs and shapes (e.g.: Pie charts, Ellimaps

- use nested ellipses of various sizes to build graph-

ics (Otjacques et al., 2009), Treemaps, Geographical

Treemaps, etc.). Depending on the purpose and mean-

ing of the processed data, one speciﬁc graph may be

easier to read and understand than another one. The

following architecture is presented to understand the

manner in which visualization may be an advantage in

the way information is selected, viewed and obtained.

Figure 2: Data Integration with Visualization.

In the solution presented above, an Information

Visualization block is used in the Integration Module

as a component in the integration process. It provides

a way to visually present the dataset information and

apply ﬁlters to them in a visual form. Based on these

facts, and because OD deals with different and hetero-

geneous data sources and multiple types of data, we

argue that Information Visualization can ease the ma-

nipulation, understanding and integration process of

DATA2014-3rdInternationalConferenceonDataManagementTechnologiesandApplications

the data that is generated and the data that is provided

by new information sources. Information Visualiza-

tion can be used to analyse and understand raw data

and metadata in both internal and external integra-

tions. Problems and difﬁculties in understanding, ﬁl-

tering and viewing information can be avoided, min-

imized and/or eliminated by applying this paradigm.

An example of how Information Visualization could

help a user to quickly visualize external integration

issues is presented in ﬁgure 3:

Figure 3: Ellimap representing museum information.

The image represents the number of museums in

certain cities of France. The information, which was

previously integrated internally, comes from four dif-

ferent data sources:

• data.gouv.fr (RepubliqueFranc¸aise, 2013) - pro-

vides the number of museums in the cities of

Bordeaux, Lyon, Nantes, Paris, Strasbourg and

Toulouse;

• ToulouseMetropole.data [donn

ees publiques]

(Communaut

eUrbaineduGrandToulouse, 2011) -

provides the number of museums in the city of

Toulouse;

• Nantes Ouverture des Donn

ees - ouverture des

donn

ees publiques (NantesM

etropole, 2011) -

provides the number of museums in the city of

Nantes;

• Strasbourg.eu et Communaut

e Urbaine (Villeet-

Communaut

eUrbainedeStrasbourg, 2013) - pro-

vides the number of museums in the city of Stras-

bourg.

The size of each ellipse presented on the graph

is proportional to the number of museums in the re-

lated city. First, observing this graph, it is easy for

the user to compare cities and identify which one(s)

has more museums. This type of data representation

is very useful when the goal is to compare the size

of datasets for example. Second, it is also possible to

analyse more closely the information in the datasets.

In this case, for example, we can see that the informa-

tion about the number of museums in Nantes can be

obtained from two different data sources: data.gouv.fr

and Nantes Ouverture des Donn

ees. This is a typical

scenario where speciﬁc information can be obtained

from different sources. For example, if a user wants to

know the number of museums in Nantes, by analyzing

quickly the information on the graph, the user can eas-

ily see the existence of data incoherence. data.gouv.fr

indicates that there are 4 museums in Nantes while

Nantes Ouverture des Donn

ees demonstrates the ex-

istence of 15 museums. A user will have to choose

one data source for this information, which means he

or she has to determine which data source is the more

reliable, which one has been modiﬁed/updated more

recently, etc. An Ellimap can be used for this purpose.

It is possible to visualize the metadata of datasets (e.g.

by the use of tooltips). The example below demon-

strates how the user may see additional information

to help him to analyse and identify the required in-

formation (e.g. identify the more reliable data source

compared to another one).

Figure 4: Ellimap used to visualize dataset’s metadata.

Another kind of graph could be used for the same

purpose complementing the information with the lo-

cation of the analysed data sources: the geographical-

weighted Map. In Figure 5, the same example as pre-

sented above is shown. The difference is that the in-

formation is organized into rectangles which are po-

sitioned according to the location of the data sources

(e.g. Nantes’ OGD source information is displayed

on the North-West side of the graph – corresponding

to the geographical location of Nantes in France; data

from Strasbourg is shown on the North-East side of

the graph, etc.).

4 CONCLUSIONS AND FURTHER

WORK

OD offers many beneﬁts, potential applications and

services to society in general. However, it also has

some constraints, barriers and issues. OD Integration

OpenDataIntegration-VisualizationasanAsset

Figure 5: Geographical Weighted Map representing mu-

seum information.

may be a complex task to accomplish and the related

challenges and issues will continue to be an impor-

tant ﬁeld of research. Besides the technical problems,

some entities - both in the private and public sectors –

continue to be reluctant to collaborate and share their

data.

Fortunately, more and more data is nevertheless

being published and is already available. Having

access to these massive quantities of information is

however not enough to realize the above-mentioned

potential. The quote of Gertrude Stein ”Everybody

gets so much information all day long that they lose

their common sense” fairly resumes the meaning of

having access to large amounts of information but be-

ing completely impotent to harness and use it because

of an incapacity to interpret and analyse it.

Governments, and private and public entities who

wish to open their data should do it in an organized

and previously agreed manner, furnishing datasets ac-

companied by metadata describing their content. Due

to the use of standards and the application of prin-

ciples to publish data over the web, Linked Open

Data may be a solution to open, share and reuse data

in distributed environments in an effective and cost-

efﬁcient way, so that it can be made available and ac-

cessed by any kind of application.

But even then, for obvious reasons, OD will con-

tinue to be a rapidly-evolving and heterogeneous data

source. Thus, Information Visualization can be a

powerful tool for aiding the OD integration process.

Its methods and means may be used to provide mech-

anisms to analyse and process large datasets rapidly

and efﬁciently, in both internal and external integra-

tion, giving a visual overview of the dataset structure

and helping the user to understand its content, detect

possible errors in datasets and data incoherencies, and

show dataset metadata so it can be used for ﬁltering,

etc.

Based on our current research, we intend to build

a Visualization platform to support complex OD inte-

gration, trying to make the whole process easier, more

effective, more intuitive and quicker. To reach this ob-

jective, the platform will use advanced and innovative

types of data representation, different kinds of graphs

and various data ﬁltering systems - e.g. development

of the new FlowerDecisionGraph.

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