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. final def eq(arg0: AnyRef): Boolean

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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12. def isTraceEnabled(): Boolean

    

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13. def log: Logger

    

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14. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    

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15. def logDebug(msg: ⇒ String): Unit

    

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16. def logError(msg: ⇒ String, throwable: Throwable): Unit

    

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17. def logError(msg: ⇒ String): Unit

    

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18. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    

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19. def logInfo(msg: ⇒ String): Unit

    

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20. def logName: String

    

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21. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    

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22. def logTrace(msg: ⇒ String): Unit

    

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23. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    

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24. def logWarning(msg: ⇒ String): Unit

    

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

    

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26. final def notify(): Unit

    

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27. final def notifyAll(): Unit

    

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28. val spark: SparkSession

    

29. def stats: RDD[String]

    

    Compute distributed RDF dataset statistics.

    Compute distributed RDF dataset statistics.

    returns

    VoID description of the given dataset

30. def statsAvgPerProperty(): RDD[(Triple, Double)]

    

    29.

    29. Average per property {int,float,time} criterion

    returns

    entities with their average values on the graph

31. def statsAvgTypedStringLength(): Double

    

    22.

    22. Average typed string length criterion.

    returns

    the average typed string length used throughout the RDF graph.

32. def statsAvgUntypedStringLength(): Double

    

    23.

    23. Average untyped string length criterion.

    returns

    the average untyped string length used throughout the RDF graph.

33. def statsBlanksAsObject(): RDD[Triple]

    

    19.

    19. Blanks as object criterion

    returns

    number of triples where blanknodes are used as objects.

34. def statsBlanksAsSubject(): RDD[Triple]

    

    18.

    18. Blanks as subject criterion

    returns

    number of triples where blanknodes are used as subjects.

35. def statsClassUsageCount(): RDD[(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

    RDD of classes used in the dataset and their frequencies.

36. def statsClassesDefined(): RDD[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

    RDD of classes defined in the dataset.

37. def statsDatatypes(): RDD[(String, Int)]

    

    20.

    20. Datatypes criterion

    returns

    histogram of types used for literals.

38. def statsDistinctEntities(): RDD[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

    RDD of distinct entities in the dataset.

39. def statsDistinctObjects(): RDD[Node]

    

    Distinct Objects
    Count distinct objects within triples.

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

    returns

    RDD of objects used in the dataset.

40. def statsDistinctSubjects(): RDD[Node]

    

    Distinct Subjects
    Count distinct subject within triples.

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

    returns

    RDD of subjects used in the dataset.

41. def statsLabeledSubjects(): RDD[Node]

    

    24.

    24. Labeled subjects criterion.

    returns

    list of labeled subjects.

42. def statsLanguages(): RDD[(String, Int)]

    

    21.

    21. Languages criterion

    returns

    histogram of languages used for literals.

43. def statsLinks(): RDD[(String, Int)]

    

    26.

    26. Links criterion.

    returns

    list of namespaces and their frequentcies.

44. def statsLiterals(): RDD[Triple]

    

    * 17.

    * 17. Literals criterion

    returns

    number of triples that are referencing literals to subjects.

45. def statsMaxPerProperty(): (Triple, Int)

    

    28.Maximum per property {int,float,time} criterion

    28.Maximum per property {int,float,time} criterion

    returns

    entities with their maximum values on the graph

46. def statsObjectVocabularies(): RDD[(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

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

47. def statsPredicateVocabularies(): RDD[(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

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

48. def statsPropertiesDefined(): RDD[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

    RDD of predicates defined in the dataset.

49. def statsPropertyUsage(): RDD[(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 RDD will be created containing all property IRI's as identifier. Afterwards, their frequencies will be computed. Filter rule : none Action : M[?p]++

    returns

    RDD of predicates used in the dataset and their frequencies.

50. def statsPropertyUsageDistinctPerObject(): RDD[(Iterable[Triple], Int)]

    

    7. Property usage distinct per object
    Count the usage of properties within triples based on objects.

    7. Property usage distinct per object
    Count the usage of properties within triples based on objects. Filter rule : none Action : M[?o] += ?p

    returns

    RDD of predicates used in the dataset and their frequencies.

51. def statsPropertyUsageDistinctPerSubject(): RDD[(Iterable[Triple], Int)]

    

    6. Property usage distinct per subject
    Count the usage of properties within triples based on subjects.

    6. Property usage distinct per subject
    Count the usage of properties within triples based on subjects. Filter rule : none Action : M[?s] += ?p

    returns

    RDD of predicates used in the dataset and their frequencies.

52. def statsSameAs(): RDD[Triple]

    

    25.

    25. SameAs criterion.

    returns

    list of triples with owl#sameAs as predicate

53. def statsSubjectVocabularies(): RDD[(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

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

54. def statsTypedSubjects(): RDD[Node]

    

    24.

    24. Typed subjects criterion.

    returns

    list of typed subjects.

55. def statsUsedClasses(): RDD[Node]

    

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

    1. Used Classes Criterion
    Creates an RDD 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

    RDD of classes/instances

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

    

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57. def toString(): String

    

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58. final def wait(): Unit

    

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

    

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

    

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