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MAPI: a software framework for distributed biomedical applications.


ABSTRACT:

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Background

The amount of web-based resources (databases, tools etc.) in biomedicine has increased, but the integrated usage of those resources is complex due to differences in access protocols and data formats. However, distributed data processing is becoming inevitable in several domains, in particular in biomedicine, where researchers face rapidly increasing data sizes. This big data is difficult to process locally because of the large processing, memory and storage capacity required.

Results

This manuscript describes a framework, called MAPI, which provides a uniform representation of resources available over the Internet, in particular for Web Services. The framework enhances their interoperability and collaborative use by enabling a uniform and remote access. The framework functionality is organized in modules that can be combined and configured in different ways to fulfil concrete development requirements.

Conclusions

The framework has been tested in the biomedical application domain where it has been a base for developing several clients that are able to integrate different web resources. The MAPI binaries and documentation are freely available at http://www.bitlab-es.com/mapi under the Creative Commons Attribution-No Derivative Works 2.5 Spain License. The MAPI source code is available by request (GPL v3 license).

SUBMITTER: Karlsson J 

PROVIDER: S-EPMC3558448 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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MAPI: a software framework for distributed biomedical applications.

Karlsson Johan J   Trelles Oswaldo O  

Journal of biomedical semantics 20130111 1


<h4>Unlabelled</h4><h4>Background</h4>The amount of web-based resources (databases, tools etc.) in biomedicine has increased, but the integrated usage of those resources is complex due to differences in access protocols and data formats. However, distributed data processing is becoming inevitable in several domains, in particular in biomedicine, where researchers face rapidly increasing data sizes. This big data is difficult to process locally because of the large processing, memory and storage  ...[more]

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