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trait TransitiveReasoner extends Profiler

An engine to compute the transitive closure (TC) for a set of triples given in several datastructures.

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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. def addTransitive(triples: Set[Triple]): Set[Triple]
  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
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    @throws( ... ) @native()
  7. def computeTransitiveClosure[A](edges: DataSet[(A, A)])(implicit arg0: ClassTag[A], arg1: TypeInformation[A]): DataSet[(A, A)]

    Computes the transitive closure for a DataSet of tuples

    Computes the transitive closure for a DataSet of tuples

    edges

    the DataSet of tuples

    returns

    a tuples containing the transitive closure of the tuples

  8. def computeTransitiveClosure(triples: DataSet[Triple]): DataSet[Triple]

    Computes the transitive closure on a DataSet of triples.

    Computes the transitive closure on a DataSet of triples. Note, that the assumption is that all triples do have the same predicate.

    triples

    the DataSet of triples

    returns

    a DataSet containing the transitive closure of the triples

  9. def computeTransitiveClosure(triples: Set[Triple]): Set[Triple]

    Computes the transitive closure on a set of triples, i.e.

    Computes the transitive closure on a set of triples, i.e. it is computed in-memory by the driver. Note, that the assumption is that all triples do have the same predicate.

    triples

    the set of triples

    returns

    a set containing the transitive closure of the triples

  10. def computeTransitiveClosureOpt(triples: DataSet[Triple]): DataSet[Triple]

    Computes the transitive closure on a DataSet of triples.

    Computes the transitive closure on a DataSet of triples. Note, that the assumption is that all triples do have the same predicate. This implementation uses the Flink iterate operator (see https://ci.apache.org/projects/flink/flink-docs-master/dev/batch/iterations.html")

    triples

    the DataSet of triples

    returns

    a DataSet containing the transitive closure of the triples

  11. def computeTransitiveClosureOptSemiNaive(triples: DataSet[Triple], predicate: Node = null): DataSet[Triple]

    Computes the transitive closure on a DataSet of triples.

    Computes the transitive closure on a DataSet of triples. Note, that the assumption is that all triples do have the same predicate. If no predicate is given, we take any triple from the dataset and use its predicate. We highly recommend to provide the predicate in order to avoid unnecessary operations.

    This implementation uses the Flink iterate operator (see https://ci.apache.org/projects/flink/flink-docs-master/dev/batch/iterations.html")

    triples

    the DataSet of triples

    predicate

    the optional predicate

    returns

    a DataSet containing the transitive closure of the triples

  12. def debug(msg: ⇒ String, o: Any, os: Any*): Unit
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  13. def debug(msg: ⇒ String, e: Throwable): Unit
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  14. def debug(msg: ⇒ String): Unit
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  15. final def eq(arg0: AnyRef): Boolean
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  16. def equals(arg0: Any): Boolean
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  17. def error(msg: ⇒ String, o: Any, os: Any*): Unit
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  18. def error(msg: ⇒ String, e: Throwable): Unit
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  19. def error(msg: ⇒ String): Unit
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  20. def finalize(): Unit
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  23. def info(msg: ⇒ String, o: Any, os: Any*): Unit
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  24. def info(msg: ⇒ String, e: Throwable): Unit
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  25. def info(msg: ⇒ String): Unit
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  26. final def isInstanceOf[T0]: Boolean
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  27. def log: Logger
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  30. val nodeKeyFct: (Node) ⇒ Int
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  32. final def notifyAll(): Unit
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  33. def profile[R](block: ⇒ R): R
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  34. final def synchronized[T0](arg0: ⇒ T0): T0
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  35. def toString(): String
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  36. def trace(msg: ⇒ String, o: Any, os: Any*): Unit
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  37. def trace(msg: ⇒ String, e: Throwable): Unit
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  40. final def wait(arg0: Long, arg1: Int): Unit
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  44. def warn(msg: ⇒ String): Unit
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