Biomedical Engineering Reference
In-Depth Information
RDF triple stores are also comparatively fast thanks to the simple and
fi xed triples data format that allows for optimized indexes. This coupled
with the inherent scalability available in many triple stores means
programs can be written that provide views derived in real time from
data present in billions of triples. Many RDF triple stores offer linear
performance gains by scaling across many computers without the
complex setup that accompanies most traditional relational database
clusters. Triple stores can also readily scale across machines, as they do
not have to support the complex data relationships associated with SQL-
type databases. This characteristic relieves the triple store of the heavy
overhead associated with locking and transactions.
All of the properties of semantic technologies including both the
fl exible, extensible formatting of data as RDF triples and the handling of
data with the types of technologies outlined above, make these
technologies an excellent foundation for next-generation search and
analysis systems for scientifi c research and related Big Data applications.
Of course, in addition to a data format and technologies to handle data,
a biomedical search and analysis system would need actual data to be a
valuable tool in a researcher's toolbox. Thankfully, there exists an
excellent set of publically available biomedical data referred to as the
Linking Open Drug Data set for use as a foundation.
19.3.1 Linking Open Drug Data
In the biomedical research and development (R&D) sector, fl exible data
integration is essential for the potential identifi cation of connections across
entity domains (e.g. compound to targets, targets to indications, pathways
to indications). However, the vast majority of data currently utilized in
biomedical R&D settings is not integrated in ways that make it possible
for researchers to intuitively navigate, analyze, and visualize these types of
interconnections. Collection, curation, and interlinking public biomedical
data is an essential step toward making the sea of Big Data more readily
accessible to the biomedical research community. To this end, a task force
within the W3C Health Care and Life Sciences Interest Group (HCLSIG)
[15] has developed the Linking Open Drug Data (LODD) set [16].
Standards for the representation of data within the LODD set have been
defi ned and all of the data have been made available in the RDF
format. The LODD is an excellent resource for the biomedical research
and development community because it provides the basis for the
interconnection of valuable biological, chemical and other relevant content
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