by Angela Guess
Jans Aasman recently wrote in Information Management, “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.”
Aasman continues, “But as valuable and as proven as these individual uses cases are they only hint at, and do not truly attest to, the full array of possibilities of databases powered by semantic graphs. Today, semantic graphs are greatly expanding the utility of data lakes in a sustainable manner. The enhanced analytic capabilities of semantic graphs are integral to cognitive computing analytics, as well as to analysis of integrated unstructured and structured data. The true potential of semantic graphs is realized in linking the entire information assets of an organization for comprehensive analytics of overall internal data as well as public data and third-party data. The underlying architecture for such an undertaking commonly involves Hadoop or some other other data lake to account for issues of scale; ontologies are required for a semantically consistent model, and terminology systems are needed to clarify terms and definitions.”
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