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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.


Tests on getDSlist and lookupStateMachineModel.
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Fig17: Tests on getDSlist and lookupStateMachineModel.

Mentions: As illustrated in Figure 17, the performance of getDSList operation remains stable with the increase of integrated data source instances (up to 200), which costs about 0.0018 ms to get data source list from the Generic API ontology. However, because we take iterative comparing and matching state machine models in the algorithm of lookupStateMachineModel, the results on locating corresponding state machine models are different. The minimum performance value remains at about 0.00037 ms while its maximum increases linearly with integrating new data sources.Figure 17


Dynamic integration of biological data sources using the data concierge.

Gong P - Health Inf Sci Syst (2013)

Tests on getDSlist and lookupStateMachineModel.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig17: Tests on getDSlist and lookupStateMachineModel.
Mentions: As illustrated in Figure 17, the performance of getDSList operation remains stable with the increase of integrated data source instances (up to 200), which costs about 0.0018 ms to get data source list from the Generic API ontology. However, because we take iterative comparing and matching state machine models in the algorithm of lookupStateMachineModel, the results on locating corresponding state machine models are different. The minimum performance value remains at about 0.00037 ms while its maximum increases linearly with integrating new data sources.Figure 17

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.