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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 extension of the generic API ontology for biological domain. (a) For dynamic construction of data source API calls. (b) For client application usages.
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Fig12: The extension of the generic API ontology for biological domain. (a) For dynamic construction of data source API calls. (b) For client application usages.

Mentions: As illustrated in Figure 12, the Generic API Ontology dynamically extends with the increase of new types of integrated biological data sources. The DataSourceType Class covers but not limits to source types such as XML, flat file, web page, web service, relational database, FTP, email, and ontology. Some biological analysis and computing functions such as Blast and Clustalw are classified into the Method Class. Subclasses of the DataType Class provide unified terms for both biological domain and computer computations. Biologists utilize these metadata to customize their specific user operations such as submitQuery and FetchData, on their interested DatasourceDataElements such as Gene, Protein, and DNA.Figure 12


Dynamic integration of biological data sources using the data concierge.

Gong P - Health Inf Sci Syst (2013)

The extension of the generic API ontology for biological domain. (a) For dynamic construction of data source API calls. (b) For client application usages.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig12: The extension of the generic API ontology for biological domain. (a) For dynamic construction of data source API calls. (b) For client application usages.
Mentions: As illustrated in Figure 12, the Generic API Ontology dynamically extends with the increase of new types of integrated biological data sources. The DataSourceType Class covers but not limits to source types such as XML, flat file, web page, web service, relational database, FTP, email, and ontology. Some biological analysis and computing functions such as Blast and Clustalw are classified into the Method Class. Subclasses of the DataType Class provide unified terms for both biological domain and computer computations. Biologists utilize these metadata to customize their specific user operations such as submitQuery and FetchData, on their interested DatasourceDataElements such as Gene, Protein, and DNA.Figure 12

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.