Biomedical Engineering Reference
In-Depth Information
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Collaborative knowledge building through de novo user-defi ned
associations: users are able to create novel associations between
entities in the system and set the visibility of those de novo associations
to private, group-based, or public. The appropriate path back to the
originating creator of an association is visible to all users to allow for
validation of user-generated associations.
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Web-based application: the system employs novel software technologies
that allow for the provision of rich user experiences through a web-
based application. Web-based applications provide a number of key
advantages including ease of administration, deployment, and updating.
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Data-driven platform: the system is data-driven and fl exible enough so
that any given instance of the system can be tailored to the specifi c
interests and needs of the administrator.
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Massively scalable: TripleMap scales both in terms of its ability to
support large-scale data sets and its ability to support large numbers of
concurrent users. A range of high-performance technologies such as
computer clustering and high-performance indexing are employed to
enable this.
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Advanced association analytics: the system allows users to perform
advanced analytics against the full underlying data network provided
by all entities and all of the associations between entities available in
the system. For example, users are able to infer high-order connections
between any two entities in the system.
19.5 TripleMap Generated Entity Master
('GEM') semantic data core
At the heart of the TripleMap system is a large-scale, high-performance
data core referred to as the TripleMap Generated Entity Master ('GEM')
(the TripleMap architecture with the central GEM data core is shown in
Figure 19.1). The data GEM contains the entire master data network
available within the TripleMap system and can be continually updated
and enhanced by TripleMap administrators as they identify more data
sources for integration. The GEM controller is used to aggregate and
integrate data from a variety of sources (e.g. RDF, fl at fi le, relational
databases, Sharepoint, RSS feeds, patent literature). TripleMap users
interact with this data core by running semantic searches and then storing
results as sets, or maps of entities and the associations between entities.
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