• Facets (new session)
• Description
• Metadata
• Settings
  • Rule: ActivityStreamsMapasEquivalentb3sb3sifpdbprdf-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://www.ontologyportal.org/inference/rules/SUMO#http://www.ontologyportal.org/inference/rules/WordNet#http://www.w3.org/2002/07/owl#ldpoplwebrel-me-authskos-transurn:det:rdf:labelurn:kidehen:glossary:inference:rulesurn:owl:inference:rules:testsvirtpivot-rulesvirtrdf-eturtle-containersvirtrdf-hashtagvirtrdf-ifpvirtrdf-labelvirtrdf-meta-entity-classvirtrdf-urlNone
    
  • Inverse Functional Properties: Disabled (fastest) Apply to subjects only Apply to objects only Apply to both subjects and objects
  • "Same As": Disabled (fastest) Apply to subjects only Apply to objects only Apply to both subjects and objects (recommended) Apply to subjects, objects and predicates (not recommended on big datasets) Apply to predicates only (special use cases only)

About: Big Data     Goto   Sponge   NotDistinct   Permalink

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Type: Concept New Facet based on Instances of this Class

Big Data is a term used to identify data that has the following characteristics (properties, attributes, features): [1] High Volume [2] High Velocity [3] High Variety . Why are these characteristics significant enough to define a genre of database management challenges and associated technology solutions? Database Management Applications have historically been designed to serve the needs of what Geoffrey Moore refers to as "Systems of Record" where the combined effects of Data Volume, Velocity, and Variety are limited. Emergence of the Web, REST Architecture, and Web Applications has created a new "Systems of Engagement" application genre (also coined by Geoffrey Moore). This application genre is characterized by Database Documents taking any combination of the following forms: [1] CSV files [2] Optimized Record Columnar (ORC) files [3] Parquet Files [4] HTTP Logs [5] OS System Logs [6] JSON (e.g., Avro Files) [7] Other structured document forms populated with engagement activity . As you can see, the database document types outlined above are different from the database documents associated with your typical RDBMS, for instance. Solution frameworks like Hadoop and Spark provide new infrastructure (Distributed Storage, Relational Abstraction Mapping & Transformation) for handling these new Data Management needs.