@article{kuiler_big_2014,
	title = {From {Big} {Data} to {Knowledge}: {An} {Ontological} {Approach} to {Big} {Data} {Analytics}},
	volume = {31},
	copyright = {© 2014 by The Policy Studies Organization},
	issn = {1541-1338},
	shorttitle = {From {Big} {Data} to {Knowledge}},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/ropr.12077/abstract},
	doi = {10.1111/ropr.12077},
	abstract = {The introduction of Big Data sets in the healthcare domain has presented opportunities to engage in analytics of very large sets containing both structured and unstructured data. With advances in information technology (IT), these data sets have become available from diverse sources at greatly increased rates. The availability of Big Data sets has introduced complexities that we must address, not only in terms of semantics and analytics but also in terms of data management, storage, and distribution. Currently, the capabilities to ingest, analyze, and manage multipetabyte data sets have underscored the limitations of our analytics capabilities supported by relational database management systems. This essay argues that an ontology-based approach to data analytics provides a practical framework to address the semantic challenges presented by Big Data sets. No ontological framework can address the operational and management requirements introduced by the availability of Big Data sets, however. There are also a number of IT architectural factors that must be considered in implementing such a framework.},
	language = {en},
	number = {4},
	urldate = {2014-07-17},
	journal = {Review of Policy Research},
	author = {Kuiler, Erik W.},
	month = jul,
	year = {2014},
	keywords = {big data, database management, health and medicine, high-tech, ICTs, IT, national governance, ontology},
	pages = {311--318},
	file = {Snapshot:files/49462/abstract.html:text/html}
}

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