ArticleBioinformation2011
An efficient approach to the deployment of complex open source information systems.
Article in Bioinformation, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Complex open source information systems are usually implemented as component-based software to inherit the available functionality of existing software packages developed by third parties. Consequently, the deployment of these systems not only requires the installation of operating system, application framework and the configuration of services but also needs to resolve the dependencies among components. The problem becomes more challenging when the application must be installed and used on different platforms such as Linux and Windows. To address this, an efficient approach using the virtualization technology is suggested and discussed in this paper. The approach has been applied in our project to deploy a web-based integrated information system in molecular genetics labs. It is a low-cost solution to benefit both software developers and end-users.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.