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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.


An example of state machine model.
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Fig13: An example of state machine model.

Mentions: In order to support dynamic data exchange, customized data schemas in biological data sources need to be decomposed into data elements that the integration system can recognize and use. The execution of this dynamic transformation depends on the knowledge in the Generic API Ontology. After the classification of biological data source API, the State Machine Model Generator is used to generate XML-based state machine models. Biologists can customize their own state machine models for their specific interests by using a downloaded State Machine Generator. The XML-based state machine models provide flexible configuration for various complex operations relevant to biologists, which requires a sequence of biological data data source API functions. The Data Concierge interprets the state machine models at run time to dynamically construct calls to each data source API in the sequence. The XML-based state machine models are based on Unimod [28] using SWITCH-technology [29], and follow Event-Condition-Action (ECA) rules, which take the form of ON Event IF Condition DO Action, to express control flows in state machines. These rules specify event trigger and guard conditions for each action. An action is executed when the triggering event occurs, if and only if the guard condition is true. In the following example of an XML-based state machine model, (Figure 13) three Generic API operations, webserviceInitialization, webserviceConstructor, and Access-Read are sequentially executed for biologist’s FetchData operation.Figure 13


Dynamic integration of biological data sources using the data concierge.

Gong P - Health Inf Sci Syst (2013)

An example of state machine model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig13: An example of state machine model.
Mentions: In order to support dynamic data exchange, customized data schemas in biological data sources need to be decomposed into data elements that the integration system can recognize and use. The execution of this dynamic transformation depends on the knowledge in the Generic API Ontology. After the classification of biological data source API, the State Machine Model Generator is used to generate XML-based state machine models. Biologists can customize their own state machine models for their specific interests by using a downloaded State Machine Generator. The XML-based state machine models provide flexible configuration for various complex operations relevant to biologists, which requires a sequence of biological data data source API functions. The Data Concierge interprets the state machine models at run time to dynamically construct calls to each data source API in the sequence. The XML-based state machine models are based on Unimod [28] using SWITCH-technology [29], and follow Event-Condition-Action (ECA) rules, which take the form of ON Event IF Condition DO Action, to express control flows in state machines. These rules specify event trigger and guard conditions for each action. An action is executed when the triggering event occurs, if and only if the guard condition is true. In the following example of an XML-based state machine model, (Figure 13) three Generic API operations, webserviceInitialization, webserviceConstructor, and Access-Read are sequentially executed for biologist’s FetchData operation.Figure 13

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.