StatsCriteria

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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6. val env: ExecutionEnvironment

   

7. final def eq(arg0: AnyRef): Boolean

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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10. final def getClass(): Class[_]

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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13. val logger: Logger

    

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14. final def ne(arg0: AnyRef): Boolean

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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17. def stats: DataSet[String]

    

    Compute distributed RDF dataset statistics.

    Compute distributed RDF dataset statistics.

    returns

    VoID description of the given dataset

18. def statsClassUsageCount(): AggregateDataSet[(Node, Int)]

    

    2. Class Usage Count Criterion
    Count the usage of respective classes of a datase, the filter rule that is used to analyze a triple is the same as in the first criterion.

    2. Class Usage Count Criterion
    Count the usage of respective classes of a datase, the filter rule that is used to analyze a triple is the same as in the first criterion. As an action a map is being created having class IRIs as identifier and its respective usage count as value. If a triple is conform to the filter rule the respective value will be increased by one. Filter rule : ?p=rdf:type && isIRI(?o) Action : M[?o]++

    returns

    DataSet of classes used in the dataset and their frequencies.

19. def statsClassesDefined(): DataSet[Node]

    

    3. Classes Defined Criterion
    Gets a set of classes that are defined within a dataset this criterion is being used.

    3. Classes Defined Criterion
    Gets a set of classes that are defined within a dataset this criterion is being used. Usually in RDF/S and OWL a class can be defined by a triple using the predicate rdf:type and either rdfs:Class or owl:Class as object. The filter rule illustrates the condition used to analyze the triple. If the triple is accepted by the rule, the IRI used as subject is added to the set of classes. Filter rule : ?p=rdf:type && isIRI(?s) &&(?o=rdfs:Class||?o=owl:Class) Action : S += ?s

    returns

    DataSet of classes defined in the dataset.

20. def statsDistinctEntities(): DataSet[Triple]

    

    16. Distinct entities
    Count distinct entities of a dataset by filtering out all IRIs.

    16. Distinct entities
    Count distinct entities of a dataset by filtering out all IRIs. Filter rule : S+=iris({?s,?p,?o}) Action : S

    returns

    DataSet of distinct entities in the dataset.

21. def statsDistinctObjects(): DataSet[Triple]

    

    Distinct Objects
    Count distinct objects within triples.

    Distinct Objects
    Count distinct objects within triples. Filter rule : isURI(?o) Action : M[?o]++

    returns

    DataSet of objects used in the dataset.

22. def statsDistinctSubjects(): DataSet[Triple]

    

    Distinct Subjects
    Count distinct subject within triples.

    Distinct Subjects
    Count distinct subject within triples. Filter rule : isURI(?s) Action : M[?s]++

    returns

    DataSet of subjects used in the dataset.

23. def statsObjectVocabularies(): AggregateDataSet[(String, Int)]

    

    32. Object vocabularies
    Compute object vocabularies/namespaces used through the dataset.

    32. Object vocabularies
    Compute object vocabularies/namespaces used through the dataset. Filter rule : ns=ns(?o) Action : M[ns]++

    returns

    DataSet of distinct object vocabularies used in the dataset and their frequencies.

24. def statsPredicateVocabularies(): AggregateDataSet[(String, Int)]

    

    31. Predicate vocabularies
    Compute predicate vocabularies/namespaces used through the dataset.

    31. Predicate vocabularies
    Compute predicate vocabularies/namespaces used through the dataset. Filter rule : ns=ns(?p) Action : M[ns]++

    returns

    DataSet of distinct predicate vocabularies used in the dataset and their frequencies.

25. def statsPropertiesDefined(): DataSet[Node]

    

    Properties Defined
    Count the defined properties within triples.

    Properties Defined
    Count the defined properties within triples. Filter rule : ?p=rdf:type && (?o=owl:ObjectProperty || ?o=rdf:Property)&& !isIRI(?s) Action : M[?p]++

    returns

    DataSet of predicates defined in the dataset.

26. def statsPropertyUsage(): AggregateDataSet[(Node, Int)]

    

    5. Property Usage Criterion
    Count the usage of properties within triples.

    5. Property Usage Criterion
    Count the usage of properties within triples. Therefore an DataSet will be created containing all property IRI's as identifier. Afterwards, their frequencies will be computed. Filter rule : none Action : M[?p]++

    returns

    DataSet of predicates used in the dataset and their frequencies.

27. def statsSubjectVocabularies(): AggregateDataSet[(String, Int)]

    

    30. Subject vocabularies
    Compute subject vocabularies/namespaces used through the dataset.

    30. Subject vocabularies
    Compute subject vocabularies/namespaces used through the dataset. Filter rule : ns=ns(?s) Action : M[ns]++

    returns

    DataSet of distinct subject vocabularies used in the dataset and their frequencies.

28. def statsUsedClasses(): DataSet[Triple]

    

    1. Used Classes Criterion
    Creates an DataSet of classes are in use by instances of the analyzed dataset.

    1. Used Classes Criterion
    Creates an DataSet of classes are in use by instances of the analyzed dataset. As an example of such a triple that will be accepted by the filter is sda:Gezim rdf:type distLODStats:Developer. Filter rule : ?p=rdf:type && isIRI(?o) Action : S += ?o

    returns

    DataSet of classes/instances

29. final def synchronized[T0](arg0: ⇒ T0): T0

    

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30. def toString(): String

    

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31. final def wait(): Unit

    

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32. final def wait(arg0: Long, arg1: Int): Unit

    

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33. final def wait(arg0: Long): Unit

    

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