=Paper=
{{Paper
|id=Vol-1320/paper_22
|storemode=property
|title=DebugIT: Ontology-mediated Layered Data Integration for Real-time Antibiotics Resistance Surveillance
|pdfUrl=https://ceur-ws.org/Vol-1320/paper_22.pdf
|volume=Vol-1320
|dblpUrl=https://dblp.org/rec/conf/swat4ls/SchoberCDEDJTPLB14
}}
==DebugIT: Ontology-mediated Layered Data Integration for Real-time Antibiotics Resistance Surveillance==
https://ceur-ws.org/Vol-1320/paper_22.pdf
DebugIT: Ontology-mediated layered Data Integration
for real-time Antibiotics Resistance Surveillance
Daniel Schobera,*,1, Remy Choquetb, Kristof Depraeterec, Frank Endersd, Philipp
Daumked, Marie-Christine Jaulentb, Douglas Teodoroe, Emilie Paschee, Christian
Lovise, Martin Boekera
a Center for Medical Biometry and Medical Informatics, Medical Center - University of Frei-
burg, Germany
dschober@ipb-halle.de, martin.boeker@uniklinik-freiburg.de
b INSERM, LIMICS, (UMR_S 1142) F-75006 Paris Université, France
remy.choquet@gmail.com, marie-Christine.Jaulent@crc.jussieu.fr
c Advanced Clinical Applications Research Group, Agfa HealthCare NV, Gent, Belgium
kristof.depraetere@agfa.com
dAVERBIS, Averbis GmbH, Freiburg, Germany
enders@averbis.com, daumke@averbis.de
eDivision of Medical Information Sciences, University Hospitals of Geneva, Switzerland
dhteodoro@gmail.com, emilie.pasche@gmail.com,
christian.lovis@hcuge.ch
1 Present address: Leibniz Institute of Plant Biochemistry, Dept. of Stress and Developmental
Biology, Weinberg 3, Tel. +49 (0) 345 5582 – 1476, 06120 Halle, Germany
Abstract. Antibiotics resistance poses a significant problem in today’s hospital
care. Although large amounts of resistance data are gathered locally, they can-
not be compared globally due to format and access diversity.
We present an ontology-based integration approach serving an EU project in
making antibiotics resistance data semantically and geographically interopera-
ble. We particularly focus on EU-wide clinical data integration for real-time an-
tibiotic resistance surveillance. The data semantics is formalized by multiple
layers of terminology-bound description logic ontologies. Local database-to-
RDF (D2R) converters, normalizers and data wrapper ontologies render hospi-
tal data accessible to SPARQL queries, which populate a mediator layer. This
semiformal data is then integrated and rendered comparable via formal OWL
domain ontologies and rule-driven reasoning applications. The presented inte-
gration layer enables clinical data miners to query over multiple hospitals which
behave like one homogeneous ‘virtual clinical information system’. We show
how cross-site querying can be achieved across borders, languages and different
data models. Aside the drawbacks, we elaborate on the unique advantages over
comparable previous efforts, i.e. tackling real-time data access and scalability.
Keywords: Ontology, Semantic Web, Data Integration, Interoperability, Anti-
biotics Resistance, Infection Monitoring, Data Linkage, Public Health Surveil-
lance, Epidemiological Monitoring, Antibacterial Drug Resistance, Antibiotics
1 Introduction
As reflected in the appearance of recent infection control projects such as Infection-
Control 20202, antibiotics resistance still poses a significant health risk with high
economic impact [1]. One of the main obstacles to effectively counteract the devel-
opment of multi-resistant bacteria is the failure to provide clinical data on infectious
diseases from different sites in real time. The DebugIT project (Detecting and Elimi-
nating Bacteria UsinG Information Technology, http://www.debugit.eu), a large scale
data integration effort funded within the EU 7th Framework Program, aimed to use
semantic web technologies to fight the increase in antibiotics resistances by means of
EU wide monitoring and surveillance [2].
In order to access and compare such distributed patient data over various nations,
languages, formats and databases, DebugIT had to find a way to harmonize and inte-
grate this heterogeneous data. To be able to monitor and alert on resistance develop-
ment, a software architecture had to be set up to access, communicate and compare
clinical data from different European hospitals. We here derive the requirements of
our integration architecture by reviewing the drawbacks of existing resistance moni-
toring programs and show how real time data access and comparison as well as sec-
* corresponding author
2 www.infectcontrol.de
ondary data usage is facilitated in the DebugIT system. Data within such a semantic
interoperability platform (SIP) has to be uniform with respect to its intended meaning
to ensure coherent interpretation by humans and processing tools.
The aim of this work is to present and analyse a layered ontology-architecture ap-
proach serving the DebugIT SIP and its Artemis resistance monitor [3]. Each formali-
zation-layer represents a step further from a concrete local and context-dependent data
source towards an integrated global, formal and unambiguous data semantics. As the
core ontologies’ creation, maintenance and design principles have been described
earlier [4], we here focus on the ontology-based data integration approach and elabo-
rate on how the ontology layers are used within the DebugIT application chain at run-
time. We show how ontological expressions are used in querying locally dispersed
resistance-data and how this data is exploited in surveillance applications feeding
clinical monitoring and real-time resistance alert systems.
2 Backgrounds
2.1 Requirements for an EU wide surveillance system
The DebugIT consortium attempts to set up an antibiotics surveillance system to be
used by medical IT personnel that alleviates the drawbacks of existing approaches.
We reviewed earlier efforts for such drawbacks (see Supplementary Material3) and
derived the following requirements for our particular use case:
a) Enabling parallel access to EU-wide hospital data.
b) Allowing for highly granular domain coverage by means of expressive ontolo-
gies of sufficient domain coverage.
c) Granting real-time data access by means of fully automatic semantic web tool-
chains.
d) Defining ontologies serving as common format for exchange syntax and domain
semantics.
e) Setting-up an automatic feed of real hospital data into knowledge bases with
formal semantics, allowing for secondary data exploitation and pattern discovery in
a timely fashion.
3 Material and Methods
3.1 The layered ontology-based data integration architecture
DebugIT is set up as a Service Oriented Architecture (SOA), where all framework
components invoke services from each other and communicate in ontologically de-
fined semantics, using ETL mechanisms. We choose an ontology-based approach [5],
3 Please find supplementary material, indicated per manuscript section, on our webpage at
msbi.ipb-halle.de/msbi/debugit
to formalize our data semantics and used the BioTop4 formal upper level ontology for
description logics (DL) constraint inheritance. Access to geographically distribute and
semantically heterogeneous data is integrated via semantic web technologies like
OWL and Notation 3 (N3) ontologies plus rules5. SPARQL endpoints are used for
data querying [6]. At the root stands a data access mechanism related to the federated
data warehouse model approach described in [7]. The interoperability backbone rep-
resents a wrapper-mediator architecture implemented in RDF syntax, which allows
for data re-usage in an Open Linked Data approach [8].
The overall ontology based interoperability architecture is based on the W3C Health
Care and Life Science (HCLS) Linked Data Guide 6, but in order to bridge the seman-
tic gap from informal database entries to ontological descriptors in formal DL, we
choose a hybrid ontology approach as described in [9], mapping local ontologies to a
global ontology for scalability reasons. A stepwise data conversion approach over two
ontology layers with different degrees of formality is applied (Fig. 1 and Supplemen-
tary Material).
The complete flow chain comprises of three levels of data integration, each of which
consist of a data representation layer and an associated mapping and query step. Thus,
the complete integration chain consists of a stack of six communication artefacts.
These levels are here sequentially identified from local relational databases to the
highest level of semantic integration on formal knowledge representations. The data
representation layer of each level is indicated by Roman numbers I - III, the directly
associated query step on top of this representation with Arabic numbers 1-3, and the
mappings between the layers are indicated via Greek letters .
On the first level of data integration, the relational database data (I) is lexically nor-
malized via mappings to medical terminologies and morphosemantic mapping em-
ploying the Averbis Morphosaurus software7. We enrich ambiguous local data with
ontological expressions in OWL on the levels II and III of the semantic integration
framework. A D2R mapping call (1) exploits a D2R mapping assignment ( to popu-
late a local but internet-accessible RDF wrapper in form of a SPARQL endpoint (II).
This level II employs so-called Data Definition Ontologies (DDO) [10], which bridge
the gap from local information models to semi-formal data on the local mediation
layer, serving syntactic integration and the ETL process,8. Here the SOA services
request the local RDF converted data (I) via SPARQL queries (2), which we call Data
Set SPARQL Queries (DSSQ).
In the next integration step (2) the local DDO data (II) is mapped onto the DebugIT
core ontology (DCO [4]), and Operational Ontologies (OO) (III) via DDO2DCO
mapping rules () in N3 format using Simple Knowledge Organization Structure
(SKOS) mappings. The particular formalization approach is chosen depending on the
4 http://bioportal.bioontology.org/ontologies/BT
5enforced via the coherent logics reasoner Euler Eye:
http://eulersharp.sourceforge.net/README#eye, last accessed 03.03.14,
6 http://www.w3.org/2001/sw/hcls/notes/hcls-rdf-guide/, last accessed 03.03.14
7 http://www.freidok.uni-freiburg.de/volltexte/4932/pdf/diss_daumke.pdf, last accessed
03.03.14
8 http://en.wikipedia.org/wiki/Extract,_transform,_load, last accessed 03.03.14
datatypes appearing in the original Clinical Information Systems (CIS). For free text
data, we exploit a rule-driven formalization, e.g. manually generated database to DDO
(D2R) mappings () followed by local-to-global DDO to DCO N3 mapping rules. For
terminologies/codes, we also use a chain of SKOS terminology mappings, e.g. ICD-
10 to SNOMED-CT and SNOMED-CT to DCO.
In accordance to [11] on the semantic integration level (III), the data is now global-
ly accessible by means of so-called Clinical Analysis SPARQL Queries (CASQ (3)),
addressing one common Domain Ontology formalized endpoint and representing an
integrated virtual Clinical Data Repository (vCDR).
For data normalization and DDO instance generation on layer II, results from text-
mining normalization approaches encoded in N3 and medical terminology mappings
(encoded via SKOS) were integrated, linking the database schema and contained data
to a common underlying individual databases. The links between data and entities in
the ontologies employed here combine ‘shallow annotation’, i.e. annotating per data-
base schemata fields, and ‘deep annotation’, i.e. annotating per database values) [12].
These two local-to-global formalization approaches are applied in parallel to create
formalized class instances in the ontologized layers II and III. The described mapping
and integration approaches are exemplified with concrete code examples in the Sup-
plementary Material.
Fig. 1. Layered DebugIT mediator architecture. A schematic overview of the data formali-
zation layers serving distributed data integration in the DebugIT interoperability platform.
Clinical data in local relational databases (I) can be accessed in real-time via SPARQL query-
ing the local endpoints via a DSSQ using DDOs on an Extract Transform Load wrapper (II).
Integrated cross-site queries can be stated as CASQ using DCO and Operational Ontologies
(OO) (III). The DDO (local) to DCO (global) binding is done via N3 mapping rules enforced
by the Euler Eye reasoner. CASQ results are fed back to the clinical researcher or into applica-
tions e.g. a resistance monitor. For each data representation layer I - III a layer-specific map-
ping process had to be employed which binds each representational layer to the succeeding
layer above. I.e., the first mapping (D2R mappings) binds the underlying relational database
layer (I) to the RDF representation layer (layer II), the next mapping (N3 and SKOS) binds
the RDF layer II to the Domain Ontology (DO) layer III. These stacked mappings enable each
semantic layer to 'interpret' the results provided by the layer one level below with a fixed se-
mantics and hence allow a user or a monitoring tool to query distributed clinical data integra-
tively.
4 Results
4.1 Generated Ontologies
The built ontologies cover a conceptual space from patient data and infectious diseas-
es to antibiotics resistance measurements. After axiomatization of these elements, the
ontologies were expanded in an iterative use-case-driven manner: We successively
iterated through the agreed set9 of competency questions, adding representational
units needed to answer these.
Seven Data definition ontologies (DDOs were generated locally and are accessible
under their respective endpoints. They define between 10 and 25 classes each. Opera-
tional ontologies (OOs) deliver semantic identifiers for the implementation of the
DebugIT framework itself, such as query building, data mining, decision support,
evidences, units, quantities, SKOS schemes and datatypes. Domain ontologies (DOs
like DCO and clinical analysis ontologies (CAO)) add description logic expressivity
SRIQ(D) [13] to the antibiotics terminology. The Hermit DL reasoner10 was used,
which takes ~10 min on an average PC to check 668 DCO classes including its 400
top level BioTop classes for consistency.
4.2 Applying DebugIT on the client side
At the moment, two end-user applications are set up to exploit the ontology-integrated
data: the monitoring dashboard for the DebugIT SIP with the knowledge authoring
tool11 and the Antimicrobial Resistance Trend Monitoring System (Artemis, an auto-
matic resistance comparison and visualization tool [3]. The SIP dashboard (Fig. 2) is
9http://www.debugit.eu/progress/documents/DebugIT%20D1.1b%2020091214%202145.pdf,
Page 47, last accessed 03.03.14
10 http://hermit-reasoner.com/, last accessed 03.03.14
11 https://debugit.agfa.net/authoring/, last accessed 03.03.14
an end users interface to interact with the DebugIT knowledge base and ask
knowledge-driven clinical questions to its participating clinical sites. The user has
three options reflected as GUI tabs, closable depending on the type of data he is inter-
ested in:
1. Select and run an existing predefined CASQ query with all template fillers pro-
vided as bound variables. The user searches in a list of predefined CASQ in a given
query-library, searching queries by query-type, keywords, author’s name, valida-
tion status or result-age. No particular skill prerequisites are demanded on the user
side.
2. Align and refine an existing CASQ query template to specific user-needs by
changing filler values (selecting specified ontology terms, Fig. 2.). To do so, users
need to understand at least the taxonomic is_a hierarchy of the domain ontologies.
3. Generate a new CASQ query with new overall semantics and store it in the re-
pository as a new custom analysis. To do this the user needs to have a deeper un-
derstanding of semantic web technologies like N3 formatted subject-predicate-
object triple statements.
Fig. 2. Question Authoring Tool. An already formalized query template can be aligned ac-
cording to a specific research question, by binding it to concrete actual variables by selecting
appropriate DO classes.
The Debug IT semantic integration platform has been evaluated against the set of
competency questions and responded to all competency questions successfully. In the
Supplementary Material, we provide an exemplary view on the semantic integration
process for a selected competency question, corresponding query template and map-
ping layers. All intermediate formal artefacts are shown and explained there.
4.3 DebugIT usage within EU hospitals
The DebugIT dashboard is used in different European hospitals to update their CIS
with the new DebugIT knowledge. The University Hospital of Geneva (HUG) for
example, consults DebugIT generated results through the Artemis monitor, although
not driving antibiotics prescription decisions directly, because of the tight quality
procedures that must be fulfilled to ensure functionality in the clinical production
system. Microbiologists at the Hôpital Européen Georges Pompidou (HEGP, Paris)
use DebugIT to drive the yearly reporting on resistance patterns in a semiautomatic
fashion12. General tool usability was measured via a short questionnaire with ten cli-
nicians using standardized interviews on a 5-point Likert scale13, which indicated that
the tool lived up to the expectations.
4.4 Applications for secondary data usage of integrated data
The formalized instances are now ready for secondary data usage in an Open Linked
Data fashion and amenable to intelligent data mining techniques. External semantic
web tools can now be applied on the integrated data, e.g. in our case, collated data is
analyzed and visualized graphically in our Pan-European resistance monitoring dash-
board, displaying selected query results as freely configurable diagrams (Fig. 3).
12 http://www.biomedcentral.com/content/pdf/1753-6561-5-S6-P320.pdf
13http://www.debugit.eu/progress/documents/DebugIT_D7_2_20110214-Dipak.pdf, last ac-
cessed 03.03.14
Fig. 3. DebugIT bacterial resistance monitor dashboard. Population monitoring is here
build around a parametrisable dashboard, where individual visualization portlets, called gadg-
ets, show the results of the CASQ SPARQL queries for the selected hospital sites at Linköping
University Hospital (LIU), University Hospital of Geneva (HUG), University Clinic Freiburg
(Averbis) and on selected additional variables. New gadgets can be dragged in, according to
each user’s needs and preferences.
5 Discussion
We have presented an ontology-based distributed data integration approach to serve
the communication channel in the DebugIT EU project, hereby making antibiotics
resistance data semantically and geographically interoperable. Although ontological
data integration was achieved, semantic formalization had commenced in a stepwise
manner. We showed how a bi-layered hybrid formalization approach can bridge the
semantic gap between local RDF converted clinical data and the common formal
integration layer. Domain ontologies representing the terminological domain of inter-
est in a computer-interpretable way ensured a common interpretation, increased its
robustness and suitability for secondary data usage. For the layer binding, we choose
a rule-driven DDO to DCO mapping method, over SPARQL Construct-to-Where
clause mappings. This decision was taken, although an existing limited performance
analysis14 highlighted SPARQL Construct-to-Where clause mappings [4] as the most
performant binding method. A key argument in favor of the N3 rule-mapping ap-
proach was the envisioned real-time handling of high-throughput data volumes. In
accordance with previous findings [14], processing of OWL axioms was considered
as too slow for the envisioned real-time handling of large data volumes. Another rea-
son for selecting N3 rules over SPARQL bindings was that rules were used within the
remainder of the SIP already and the burden of writing correct rules could be alleviat-
ed partly by checking generated rules automatically.
5.1 Comparison to other ontology-based integration efforts
In [15] a knowledge base (KB) is described serving a rule-driven clinical decision
support system (CDS) for guiding antibiotics prescription. Although its main scope is
error-detection in patient-centric hospital care, its underlying ontology captures con-
cepts overlapping with the DebugIT scope, i.e. 'antibiotics coverage range'. This CDS
is however site-specific and only considers local medical data whereas the DebugIT
System considers resistance-centric data from all over Europe and is set up in an ex-
tensible way allowing multiple new sites to participate in a seamless and scalable
manner. Another difference is the data gathering method. Whereas for the CDS, the
instance data was fed into the KB manually, in DebugIT we set up an automatic Ex-
tract, Transform, Load (ETL) data feed from the site-specific production databases,
rendering the accessible data up-to-date over time. For the above reasons and due to
the fact that the whole system is implemented as plugin for the Protégé 4 ontology
editor, the CDSs' general setup is less complicated. On the downside, however medi-
cal doctors have to work with quite a complex tool and GUI, whereas in DebugIT
these could be kept simple and easy as they were developed proprietarily [3] shielding
the end-user from underlying complexity.
In its general set-up, our approach is similar to the OpenFlyData project15 in that it
uses semantic web technologies, integrating data for a specific domain. OpenFlyData
also integrates distributed data via ETL, D2R servers and SPARQL endpoints and
provides query templates. OpenFlyData however uses a single ontology layer for
terminological mapping and tackles the entirely different domain of Fly gene mapping
and expression analysis.
Regarding the implementation of the hybrid ontology layer approach the DebugIT
SIP architecture closely resembles that of the NASA "SIMA: SemanticIntegrator for
Mobile Agents" project16, which integrated multiple heterogeneous sources via wrap-
pers, a data source mediator and rule-enabled ontology integration.
As in the Advancing Clinico-Genomic Trials on Cancer (ACGT) project [16],
which aims at improving post-genomic clinical trials by providing seamless access to
14 comparing rules with OWL axiom and SPARQL query based cross-data mappings
15 http://intranet.cs.man.ac.uk/dils09//presentations/2009_dils_flyweb.pdf, slide 8, last accessed
03.03.14
16http://ti.arc.nasa.gov/m/pub-archive/1221h/1221%20%28Keller%29.pdf, last accessed
03.03.14
integrated clinical, genetic, and image databases, we use information model-derived
mediator artefacts and SPARQL to resolve syntactic and semantic heterogeneities
when accessing wrapped databases. Like DebugIT, both the latter projects maintain
separate SPARQL endpoint for each data source, which makes data updating easier
than in data warehouse approaches. We wanted to be able to cope with dynamics and
changes on all levels, e.g. updates to data, evolving schemas, and new CIS sources to
be added. Classic DB based data warehouses were hence no option.
The functionalities of the web-based ResistanceMap17 for interactive exploration of
antimicrobial surveillance indicators are partly overlapping with that of the DebugIT
dashboard. Whereas ResistanceMap mainly covers maps visualizing data from the
U.S., our dashboard concentrates on European data. Both resources allow for national
resistance comparisons, and provide graphics, which can be re-embedded on blogs or
websites. However, besides DebugITs richer query answering capabilities, we believe
our real-time surveillance can identify trends in pathogen incidence and antimicrobial
resistance much faster than monitoring set-ups with larger time delays. An additional
value of the DebugIT ontology based data integration is its eased secondary data us-
age and re-purposing.
5.2 Advantages and Limitations of our approach
The known overall drawbacks of our wrapper-mediator architecture are the query
complexity and diminished source transparency. Although it is possible to achieve
similar results using non-ontological ad hoc transformations of the source data, this
kind of solution will always be context dependent and not explicit nor generic. Fur-
thermore its results cannot be automatically proven to be correct, which is, at the end
the most important issue in our case, as DebugIT attempts to enhance patient safety.
The DebugIT overall application scope, concentrating on clinical data, can still be
seen as limited, as a comprehensive system would need tackle the domain in a holistic
manner, i.e. need to cover veterinary resistance occurrences, as well as other, some-
times unexpected, but relevant hot spots of resistance occurrence, like drainages of
pharmaceutical companies in developing countries [17]. With our research, momen-
tarily we only show feasibility and practicability of the technical approach, before
going for larger domain coverage. Providing direct evidence on the effectiveness of
the general framework to patient centered outcomes must be the objective of future
research.
An exemplary result comparison to existing surveillance efforts, - the German Paul
Ehrlich Society (PEG) Monitoring and the French InVS surveillance studies, has
shown evidence for good alignment of resistance trends shown by DebugIT with the
ones indicated in the investigated surveillance efforts (see Supplementary Material).
With respect to the reviewed surveillance set-ups, the DebugIT approach revealed the
following key advantages:
17 http://www.cddep.org/resistancemap, last accessed 03.03.14
Most existing antibiotics monitoring systems suffer from low timely resolution.
The DebugIT monitoring tools access hospital data in real-time, hence allowing for
early trend discovery and opportunities for early interventions, as was demanded in
[18].
DebugIT is not limited to a certain selected set of bacteria or sampling methods,
but potentially includes all bacteria and strains as found in the hospital data that
can be mapped to a DCO/Uniprot taxon. This advantage can be generalized to a
potentially higher overall data granularity, allowing for more concise data analysis.
DebugIT stores data in a re-useable, semantically formal and traceable way, ad-
dressing the main reason for the current lack of information re-use namely data
heterogeneity and inaccessibility. Current hospital data is often stored in proprie-
tary and unconnected data silos and in diverse formats and languages.
The heterogeneity in the resistance measurement results in difficulties to compare
international resistance data and introduces a blur into the integrated data quality. The
main factor hindering harmonization of monitoring efforts is a lack of a commonly
agreed definition for resistance and epidemiological cut-off values, as these values are
compound and methodology dependent [19]. A look at an analysis on the validity and
potential bias of surveyed resistance data [20] can prove useful to avoid inclusion of
low impact data and will foster agreement what to include in the respective ontolo-
gies, rendering data validity more robust.
Besides the clinical queries to be solved as demanded by our clinical advisory
board, the twelve identified quality indicators defined by the European Surveillance
of Antimicrobial Consumption Network (ESAC-Net)18 should serve validation. The
importance of query contents for specific countries for example should be scored for
their relevance on resistance and public health policies.
6 Conclusion
We have developed an IT framework to address the integrated management of clinical
antibiotics data and detect patient safety related patterns and trends. To fight the epi-
demic spread of resistant pathogens due to citizen mobility we focus on EU wide and
potentially global data inclusion. We have outlined an ontology-driven approach,
leveraging on semantic web technologies, to create an open expandable and large-
scale antibiotic resistance surveillance system across internet resources. We showed
how ontologies provide computer-interpretable semantics and standard exchange
syntax to be exploited for heterogeneous data integration, abstract querying, data
comparison and reuse in an EU-wide antibiotics monitoring and alert system, easing
secondary data usage. We showed in real world examples how cross-site data access,
exchange and comparison is made possible via a three step layered integration ap-
proach, bridging from a local RDF representation over local data definition ontologies
18 http://www.ecdc.europa.eu/en/activities/surveillance/ESAC-
Net/publications/Documents/antimicrobial-consumption-ESAC-Net-reporting-protocol-
2014.pdf
towards formal domain ontologies and rules as semantic integrators. The DebugIT
ontologies were shown to drive a semantic interoperability platform to federate heter-
ogeneous data from different hospital information systems into one formalized re-
source called ‘virtual CDR’. We exemplified feasibility of our layered data integra-
tion approach in an ‘ITbiotics’ application.
Although the presented multi-layered data integration approach is complex and re-
quires considerable technological experience, we believe it is feasible. So far, it repre-
sents the only scalable solution, enabling seamless integration of additional hospital
sites while maintaining local autonomy of present data sources.
In general, we have contributed to access the deep web19, as we make hidden data
web-searchable and we hope the DebugIT approach will serve as a model for a Euro-
pean bio-surveillance networks providing real-time monitoring tools.
7 Authors’ contributions
DS lead the DebugIT WP1a, developed the DebugIT core ontology, contributed to the
SIP and prepared the manuscript. RC and MB contributed to the SIP, helped develop-
ing the DCO and contributed to the manuscript. KD implemented the DebugIT dash-
board and contributed to the SIP architecture. FE and PD were responsible for the
lexical mappings. DT, EP and CL implemented the Artemis monitor. All authors con-
tributed to the manuscript.
8 Acknowledgements
This research is funded by the DebugIT project of the EU 7th Framework Program
grant agreement ICT-2007.5.2-217139. We thank Hans Cools, Dirk Colaert and Gio-
vanni Mels for their leading participation in the DebugIT project.
9 Conflict of Interest
We declare that there are no conflicts of interest.
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