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DTSTAMP:20260608T120218Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2026.data
 versity.net/sessionPop.cfm?confid=165&proposalid=16419\nGraph analytics ena
 bles organizations to perform complex functions like fraud detection, custo
 mer influence networks, and recommendation engines. However, designing thes
 e solutions at scale requires careful consideration towards graph modeling,
  integration interoperability, and semantic standards. The underpinnings th
 at form semantic layers–metadata, taxonomies, ontologies, and graph d
 atabases–also power successful graph analytics. Consequently, semanti
 c layers are essential to implementing graph analytics solutions in product
 ion.\n\nKyle Garcia and Nick Sacoman of Enterprise Knowledge introduce grap
 h analytics, outline what a typical solution entails, and explain how integ
 rating a semantic layer into graph analytics enhances insight generation. T
 hey explain how businesses can discover hidden connections in enterprise da
 ta, enable context-aware information enrichment, and support continuous int
 egration of new data sources. Garcia and Sacoman illustrate this approach i
 n a real-world case study where EK delivered a graph analytics solution, wi
 th a semantic layer, to a national fraud investigator, enabling suspicious 
 pattern identification through an interactive graph visualization interface
 .\n
DTSTART:20260506T114500
SUMMARY:Semantics and Schemes: Graph Analytics and the Semantic Layer
DTEND:20260506T122959
LOCATION: See Description
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