Modeling your data as a graph has a significant advantage: The schema does not need to be explicitly defined or specified ahead of time. Thus, you can add data to your graph without being constrained by any schema. One of the less recognized problems with data addition to a graph, however, is the potential for loss of backward compatibility with regard to queries designed before the changes are made to the data. Use of RDF Quads (W3C RDF1.1 Recommendation 25-FEB-2014) as your graph data model would allow schema evolution caused by data addition to your graph to preserve backward compatibility of pre-existing queries.
Graphs provide a new dimension to managing and analyzing data, and enterprises are keen to explore and adopt this technology. There have been some barriers to adoption, including a lack of familiarity with graph query languages and tools and challenges in integrating graph analytics into existing workflows without using specialized silos. We will illustrate customer use cases from three different industries and see how they overcome some of these challenges to successfully deploy solutions based on graphs, enabling significant impact on their businesses. The use cases are the use of RDF for a semantic terminology server in Pharma, use of RDF for linking public data sets (Department of National Statistics in Japan), and use of Property Graphs for fraud detection (Paysafe, an online payments solutions company).
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