Class

net.sansa_stack.ml.spark.clustering.utils

DataProcessing

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class DataProcessing extends Serializable

load TomTom dataset

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Instance Constructors

  1. new DataProcessing(spark: SparkSession, conf: Config, dataRDD: RDD[Triple])

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    spark

    SparkSession

    conf

    Configuration

Value Members

  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 conf: Config

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    Configuration

  7. def createSubjects(poiID: Long): ArrayBuffer[String]

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    poiID

    id of a poi

    returns

    an array of subject in RDF triples with related to this poi

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

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  9. def equals(arg0: Any): Boolean

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  10. def filterCoordinates(poiCoordinates: RDD[(Long, CoordinatePOI)], lo_min: Double, lo_max: Double, la_min: Double, la_max: Double): RDD[(Long, CoordinatePOI)]

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    poiCoordinates

    super set of poi with coordinates

    lo_min

    min longitude

    lo_max

    max longitude

    la_min

    min latitude

    la_max

    max latitude

    returns

    pois within certain coordinates

  11. def finalize(): Unit

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  12. def getCategoryId(poiCoordinates: RDD[(Long, CoordinatePOI)], poiFlatCategoryId: RDD[(Long, Long)]): RDD[(Long, Set[Long])]

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    get (poi_unique, poi_category_id_set)

    get (poi_unique, poi_category_id_set)

    poiCoordinates

    (poi_unique, Coordinate)

    poiFlatCategoryId

    (poi, category_id)

  13. def getCategoryValues: RDD[(Long, Categories)]

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    get (category_id, Categories)

    get (category_id, Categories)

    returns

    RDD with category values for category id

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

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  15. def getPOICategories(poiCoordinates: RDD[(Long, CoordinatePOI)], poiFlatCategoryId: RDD[(Long, Long)], poiCategoryValueSet: RDD[(Long, Categories)]): RDD[(Long, Categories)]

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    get (poi_unique, Categories)

    get (poi_unique, Categories)

    poiCoordinates

    (poi_unique, Coordinate)

    poiFlatCategoryId

    (poi, category_id)

    poiCategoryValueSet

    (category_id, Categories)

    returns

    (poi, Categories)

  16. def getPOICoordinates(lo_min: Double, lo_max: Double, la_min: Double, la_max: Double): RDD[(Long, CoordinatePOI)]

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    load data filter on geo-coordinates

    load data filter on geo-coordinates

    lo_min

    min longitude

    lo_max

    max longitude

    la_min

    min latitude

    la_max

    max latitude

  17. def getPOICoordinates: RDD[(Long, CoordinatePOI)]

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    get coordinate for all poi

  18. def getPOIFlatCategoryId: RDD[(Long, Long)]

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    returns

    (poi, category_id)

  19. def getYelpCategories(mergedRDD: RDD[Triple]): RDD[(Long, (Categories, Double))]

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  20. def get_triples(a: String, poiArray: Array[Long], dataRDD: RDD[Triple], spark: SparkSession): RDD[(String, Triple)]

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  21. def hashCode(): Int

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  22. final def isInstanceOf[T0]: Boolean

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  23. def loadNTriple(tripleFilePath: String): RDD[Triple]

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

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  25. final def notify(): Unit

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  26. final def notifyAll(): Unit

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  27. var poiCategories: RDD[(Long, Categories)]

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  28. var poiCategoryId: RDD[(Long, Set[Long])]

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  29. var poiCategoryValueSet: RDD[(Long, Categories)]

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  30. var poiCoordinates: RDD[(Long, CoordinatePOI)]

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  31. var poiFlatCategoryId: RDD[(Long, Long)]

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  32. val poiYelpCategories: RDD[(Long, (Categories, Double))]

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  33. var pois: RDD[POI]

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  34. val spark: SparkSession

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    SparkSession

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

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  36. def toString(): String

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  37. final def wait(): Unit

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

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

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