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Dynamic integration of biological data sources using the data concierge.

Gong P - Health Inf Sci Syst (2013)

Bottom Line: Experimental results demonstrate that for obtaining dynamic features, the Data Concierge sacrifices reasonable performance on reasoning knowledge models and dynamically doing data source API invocations.The overall costs to integrate new biological data sources are significantly lower when using the Data Concierge.The Data Concierge facilitates the rapid integration of new biological data sources in existing applications with no repetitive software development required, and hence, this mechanism would provide a cost-effective solution to the labor-intensive software engineering tasks.

View Article: PubMed Central - PubMed

Affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, the University of Sydney, Sydney, NSW 2006 Australia ; Department of PET and Nuclear Medicine, RPA Hospital, Camperdown, NSW 2050 Australia.

ABSTRACT

Background: The ever-changing landscape of large-scale network environments and innovative biology technologies require dynamic mechanisms to rapidly integrate previously unknown bioinformatics sources at runtime. However, existing integration technologies lack sufficient flexibility to adapt to these changes, because the techniques used for integration are static, and sensitive to new or changing bioinformatics source implementations and evolutionary biologist requirements.

Methods: To address this challenge, in this paper we propose a new semantics-based adaptive middleware, the Data Concierge, which is able to dynamically integrate heterogeneous biological data sources without the need for wrappers. Along with the architecture necessary to facilitate dynamic integration, API description mechanism is proposed to dynamically classify, recognize, locate, and invoke newly added biological data source functionalities. Based on the unified semantic metadata, XML-based state machines are able to provide flexible configurations to execute biologist's abstract and complex operations.

Results and discussion: Experimental results demonstrate that for obtaining dynamic features, the Data Concierge sacrifices reasonable performance on reasoning knowledge models and dynamically doing data source API invocations. The overall costs to integrate new biological data sources are significantly lower when using the Data Concierge.

Conclusions: The Data Concierge facilitates the rapid integration of new biological data sources in existing applications with no repetitive software development required, and hence, this mechanism would provide a cost-effective solution to the labor-intensive software engineering tasks.

No MeSH data available.


The generic API ontology for the semantic description of data source API.
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Fig10: The generic API ontology for the semantic description of data source API.

Mentions: The challenges caused by unpredictable changes could be tackled if the integration system were able to dynamically connect and invoke previously unknown APIs on the basis of the semantics of source functionalities. The top-level model of the first part in the ontology (Figure 10) is used by the Data Concierge to dynamically construct calls to data sources APIs. It represents the semantics of the data source API and associated data schema. The classified metadata in the Generic API ontology shield the heterogeneities of low-level source interfaces and data models from the Data Concierge middleware and client applications. After classifying data source APIs and data schemas into the Generic API Ontology with the Data Source API Description Tool, these metadata help the Data Concierge to discover and invoke the desired biological data source functionalities for accomplishing biologist manipulations.Figure 10


Dynamic integration of biological data sources using the data concierge.

Gong P - Health Inf Sci Syst (2013)

The generic API ontology for the semantic description of data source API.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4340781&req=5

Fig10: The generic API ontology for the semantic description of data source API.
Mentions: The challenges caused by unpredictable changes could be tackled if the integration system were able to dynamically connect and invoke previously unknown APIs on the basis of the semantics of source functionalities. The top-level model of the first part in the ontology (Figure 10) is used by the Data Concierge to dynamically construct calls to data sources APIs. It represents the semantics of the data source API and associated data schema. The classified metadata in the Generic API ontology shield the heterogeneities of low-level source interfaces and data models from the Data Concierge middleware and client applications. After classifying data source APIs and data schemas into the Generic API Ontology with the Data Source API Description Tool, these metadata help the Data Concierge to discover and invoke the desired biological data source functionalities for accomplishing biologist manipulations.Figure 10

Bottom Line: Experimental results demonstrate that for obtaining dynamic features, the Data Concierge sacrifices reasonable performance on reasoning knowledge models and dynamically doing data source API invocations.The overall costs to integrate new biological data sources are significantly lower when using the Data Concierge.The Data Concierge facilitates the rapid integration of new biological data sources in existing applications with no repetitive software development required, and hence, this mechanism would provide a cost-effective solution to the labor-intensive software engineering tasks.

View Article: PubMed Central - PubMed

Affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, the University of Sydney, Sydney, NSW 2006 Australia ; Department of PET and Nuclear Medicine, RPA Hospital, Camperdown, NSW 2050 Australia.

ABSTRACT

Background: The ever-changing landscape of large-scale network environments and innovative biology technologies require dynamic mechanisms to rapidly integrate previously unknown bioinformatics sources at runtime. However, existing integration technologies lack sufficient flexibility to adapt to these changes, because the techniques used for integration are static, and sensitive to new or changing bioinformatics source implementations and evolutionary biologist requirements.

Methods: To address this challenge, in this paper we propose a new semantics-based adaptive middleware, the Data Concierge, which is able to dynamically integrate heterogeneous biological data sources without the need for wrappers. Along with the architecture necessary to facilitate dynamic integration, API description mechanism is proposed to dynamically classify, recognize, locate, and invoke newly added biological data source functionalities. Based on the unified semantic metadata, XML-based state machines are able to provide flexible configurations to execute biologist's abstract and complex operations.

Results and discussion: Experimental results demonstrate that for obtaining dynamic features, the Data Concierge sacrifices reasonable performance on reasoning knowledge models and dynamically doing data source API invocations. The overall costs to integrate new biological data sources are significantly lower when using the Data Concierge.

Conclusions: The Data Concierge facilitates the rapid integration of new biological data sources in existing applications with no repetitive software development required, and hence, this mechanism would provide a cost-effective solution to the labor-intensive software engineering tasks.

No MeSH data available.