Many aspects of data management—particularly concerning big data—hinge upon the utility of graph databases. When deployed with additional semantic technologies such as ontologies, taxonomies and vocabularies, there are few analytic feats an RDF graph cannot achieve. In most instances, end users are largely unaware of the degree of complexity that semantic graphs account for when linking and contextualizing disparate data elements for unified results.
Graph databases initially gained prominence with use cases involving social media and facets of sentiment analysis; this technology gained credence by provisioning ‘360 degree views’ of customer and product information in MDM systems. Other commonly found uses of graph databases include applications of time-sensitive data such as recommender engines for e-commerce, fraud detection for finance, search engine augmentation, and ERP optimization.
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