Biomedical Engineering Reference
In-Depth Information
workers', is emerging to conduct exactly this type of Big Data analytical
activity for both public and private organizations. However, Big Data
also presents signifi cant challenges related to the management, integration,
updating, and visualization of information at this scale. Due to these
challenges, computational tools that enable users to manipulate, visualize,
and share in the analysis of data are currently, and will become ever-
more, important for enabling the derivation of knowledge and insight
from Big Data. In particular, software tools that allow users to share and
collaborate in the derivation of high-level knowledge and insight from
the vast sea of data available are critical. The need for such tools is
particularly pressing in the biomedical research and development space
where the integration and understanding of relationships between
information from multiple disparate data sources is crucial for accelerating
hypothesis generation and testing in the pursuit for disease treatments.
19.2 Semantic technologies
A range of software tools have been developed over the past couple of
decades to address the growth of Big Data across many sectors. Highly
prevalent and successful text-based search tools such as Google [8],
Endeca [9], FAST [10], and Lucene/Solr [11] (and discussed in Chapter 14
by Brown and Holbrook) enable users to conduct key word searches
against unstructured text repositories with the results of these searches
being lists of matching text documents. Although this sort of search
paradigm and the tools that enable it are highly valuable, these tools have
a number of attributes that are not optimally suited to Big Data scenarios.
These attributes include:
■
As the amount of data grows, the results derived from text-based
search systems can themselves be so numerous that the user is left
needing to manually sift through results to fi nd pertinent information.
■
Text-based search systems do not provide a means with which to
understand the context and interconnections between resulting text.
This is because results are generated through key word matching, not
a formalized representation of the things (i.e. entities) that a user may
search for, their properties and the associations between them.
■
Certain disciplines, such as biomedical research and development, are
confronted with the challenge that any given entity of interest may
have many names, synonyms, and symbols associated with it. Because
text-based search systems are based on key word matching, users can
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