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Why property graphs fall short
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Can your graph software handle…?
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For simple graph oriented data relationships, a non-semantic (or property graph) database approach might solve a single dimensional problem like: shortest path, one-to-many relationships, weighted elements, structured inter-relationships.
But rarely are problems and queries that simple. Real world data is highly complex, multi-dimensional and needs the powerful additional features of a semantic graph solution.
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• SPARQL 1.1 query language support• Linked Open Data support – with RDF• Unstructured, free field text and document processing• Geo-spatial queries• Temporal queries• Social Network Analysis• Reasoning/Inferencing• Machine learning• Bayesian belief networks/probabilistic processing
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• Prolog with complex rule and logic processing
• Federated, multi-database integration
• Predictive analytics
• Hadoop/Cassandra/MongoDB support for massive data/document stores
• Dis-ambiguation of terms, names, locations
• JSON/JSON-LD
• SOLR – native faceted search
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Also, property graphs require the fixed definition of classes/objects, types and nodes – basically a fixed schema. If the data or data sources ever change, those changes need to be coded before any new data can be accessed.
Comparison of Property Graphs and Semantic Graphs
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AllegroGraph turns complex data into actionable business insights