• Facets (new session)
• Description
• Metadata
• Settings
  • Rule: asEquivalentb3sb3sifpdbprdf-labelfacetshttp://dbpedia.org/resource/inference/rules/dbpedia#http://dbpedia.org/resource/inference/rules/opencyc#http://dbpedia.org/resource/inference/rules/umbel#http://dbpedia.org/resource/inference/rules/yago#http://dbpedia.org/schema/property_rules#http://localhost:8890/dataspacehttp://www.ontologyportal.org/inference/rules/SUMO#http://www.ontologyportal.org/inference/rules/WordNet#http://www.w3.org/2002/07/owl#ldpoplwebskos-transvirtrdf-labelvirtrdf-urlNone
    
  • Inverse Functional Properties:
  • "Same As": Disabled (fastest) Apply to subjects, objects and predicates (not recommended on big datasets)

Data mesh is an organizational and architectural paradigm for managing big data that originated in 2019. It is a process and architectural approach that delegates responsibility for specific data sets to members of a business that have the subject matter expertise to know what the data is supposed to represent and how it should be used. The name Data Mesh comes from Mesh Networking, as this new model is also based on a decentralized architecture and proposes to move beyond a monolithic Data Lake. With Data Mesh, instead of assuming that data will reside in a data lake, each area of business is responsible for choosing how to host and serve the datasets that they own.