Ontology Driven Data Integration in Heterogeneous Networks

Cruz, Isabel F.; Xiao, Huiyong

doi:10.1007/978-3-540-88075-2_4

Cited by 34 publications

(16 citation statements)

References 35 publications

(41 reference statements)

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“…The system’s semantic interoperability schema is based on an ontology-driven data integration approach[32], where multiple semantically flat local data definition ontologies are mapped to a common domain ontology, the DebugIT Core Ontology [33]. Semantic mappings at local and global levels align concepts from the local ontologies with the domain knowledge.…”

Section: Methodsmentioning

confidence: 99%

Building a Transnational Biosurveillance Network Using Semantic Web Technologies: Requirements, Design, and Preliminary Evaluation

Teodoro¹,

Pasche²,

Gobeill³

et al. 2012

J Med Internet Res

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BackgroundAntimicrobial resistance has reached globally alarming levels and is becoming a major public health threat. Lack of efficacious antimicrobial resistance surveillance systems was identified as one of the causes of increasing resistance, due to the lag time between new resistances and alerts to care providers. Several initiatives to track drug resistance evolution have been developed. However, no effective real-time and source-independent antimicrobial resistance monitoring system is available publicly.ObjectiveTo design and implement an architecture that can provide real-time and source-independent antimicrobial resistance monitoring to support transnational resistance surveillance. In particular, we investigated the use of a Semantic Web-based model to foster integration and interoperability of interinstitutional and cross-border microbiology laboratory databases.MethodsFollowing the agile software development methodology, we derived the main requirements needed for effective antimicrobial resistance monitoring, from which we proposed a decentralized monitoring architecture based on the Semantic Web stack. The architecture uses an ontology-driven approach to promote the integration of a network of sentinel hospitals or laboratories. Local databases are wrapped into semantic data repositories that automatically expose local computing-formalized laboratory information in the Web. A central source mediator, based on local reasoning, coordinates the access to the semantic end points. On the user side, a user-friendly Web interface provides access and graphical visualization to the integrated views.ResultsWe designed and implemented the online Antimicrobial Resistance Trend Monitoring System (ARTEMIS) in a pilot network of seven European health care institutions sharing 70+ million triples of information about drug resistance and consumption. Evaluation of the computing performance of the mediator demonstrated that, on average, query response time was a few seconds (mean 4.3, SD 0.1×102 seconds). Clinical pertinence assessment showed that resistance trends automatically calculated by ARTEMIS had a strong positive correlation with the European Antimicrobial Resistance Surveillance Network (EARS-Net) (ρ = .86, P < .001) and the Sentinel Surveillance of Antibiotic Resistance in Switzerland (SEARCH) (ρ = .84, P < .001) systems. Furthermore, mean resistance rates extracted by ARTEMIS were not significantly different from those of either EARS-Net (∆ = ±0.130; 95% confidence interval –0 to 0.030; P < .001) or SEARCH (∆ = ±0.042; 95% confidence interval –0.004 to 0.028; P = .004).ConclusionsWe introduce a distributed monitoring architecture that can be used to build transnational antimicrobial resistance surveillance networks. Results indicated that the Semantic Web-based approach provided an efficient and reliable solution for development of eHealth architectures that enable online antimicrobial resistance monitoring from heterogeneous data sources. In future, we expect that more health care institutions can join t...

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Section: Methodsmentioning

confidence: 99%

Building a Transnational Biosurveillance Network Using Semantic Web Technologies: Requirements, Design, and Preliminary Evaluation

Teodoro¹,

Pasche²,

Gobeill³

et al. 2012

J Med Internet Res

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“…The use of shared or standard ontologies as a possible solution to the semantic interoperability problem has been studied for over a decade. They have also been found to provide reasonable results for semantic interoperability [17,3,18]. In a market place setting, where terminology is not well defined and varied, there will be a need to provide users with a freedom of choice in terms of how they structure their concepts or semantics and their preferred representation language.…”

Section: Semantic Interoperability and Integrationmentioning

confidence: 98%

Service and model-driven dynamic integration of health data

Taweel

Speedie

Tyson

et al. 2011

Proceedings of the First International Workshop on Managing Interoperability and Complexity in Health Systems

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Recent advances in informatics and computing are increasingly becoming essential factors for realising major improvement in healthcare. The delivery of the right information about the right patient at the point of care is central to a well-integrated healthcare process. However, the high sensitivity of the clinical domain and the vast differences in health data and systems pose a great interoperability challenge for solutions that do not employ strong semantic principles as core to the interoperation process to sufficiently scale. This paper presents a model-based approach that utilises domain ontologies combined with extensible problem models, driven by rich domain terminology and knowledge services at the centre of the process to enable autonomous data governance and semantic interoperability. The paper addresses the resulting requirements and proposes a solution outlining the results from the prototype of the approach.

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“…The data integration was defined by [7] as: "the combination of data from different sources with unified access to the data for its users". Apparently, many researchers proposed methods and models to integrate the building information with the live data to facilitate the maintenance of the buildings.…”

Section: Related Research Workmentioning

confidence: 99%

Utilising Latent Data in Smart Buildings: A Process Model to Collect, Analyse and Make Building Data Accessible for Smart Industries

Pourzolfaghar

Helfert

2017

Annals of Computer Science and Information Systems

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Abstract-Smart buildings are embedded with large amounts of latent data from different sources, e.g. IoT devices, sensors, and the like. Integration of this latent data with the buildings information can highly impact efficiency of the services provided by various industries, e.g. facility management companies, utility companies, smart commerce, and so forth. To manage buildings information, diverse technologies such as Building Information Modelling (BIM) technology have been developed and changed the traditional approaches. Notwithstanding a plethora of research in this area, potential users of this information, such as facility management companies, are still unable to fully benefit from the building information. This is due to the fact that various information and data have been heterogeneously scattered across various sources. To overcome this challenge, this research follows the design science approach to propose a process model to enable facility management industry to access the integration of buildings information with live data. The presented process model is introduced thoroughly by explaining the required steps to collect and integrate and preserve the integrated information and data. The evaluation of the process model was undertaken via the employment of a focus group session with the construction professionals, the IoT experts, and the data analysts. Also, this paper elaborates on two industrial use-cases to demonstrate how having access to the building information effectively affects the other industries. The outcome of this research provides an open access to the integration of building information and live data for diverse range of users.

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Ontology Driven Data Integration in Heterogeneous Networks

Cited by 34 publications

References 35 publications

Building a Transnational Biosurveillance Network Using Semantic Web Technologies: Requirements, Design, and Preliminary Evaluation

Building a Transnational Biosurveillance Network Using Semantic Web Technologies: Requirements, Design, and Preliminary Evaluation

Service and model-driven dynamic integration of health data

Utilising Latent Data in Smart Buildings: A Process Model to Collect, Analyse and Make Building Data Accessible for Smart Industries

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