class CONOD extends Serializable
This is the modified version of CONOD which can work on any arbitrary RDF data. The original CONOD was only working on DBpedia. By changing the similarity function now it is generic
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new
CONOD(spark: SparkSession, originalDataRDD: RDD[Triple], config: DistADConfig)
- spark
the initiated Spark session
- originalDataRDD
the data
- config
the config object
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def
iqr(data: Seq[(String, String, Double)], anomalyListLimit: Int): Seq[(String, String, Double)]
Anomaly Detection method based on Interquartile Range
Anomaly Detection method based on Interquartile Range
- data
a given data
- anomalyListLimit
the min value list size for considering a list for anomaly detection process
- returns
list of datapoints which are anomalies
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final
def
isInstanceOf[T0]: Boolean
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- def jSimilarity(TriplesWithNumericLiteral: RDD[Triple], mapSubWithTriples: RDD[(String, Set[(String, String, Double)])]): RDD[Set[(String, String, Double)]]
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- def propClustering(triplesWithNumericLiteral: RDD[Triple]): RDD[(String, Set[(String, String, Double)])]
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def
run(): DataFrame
The main function
The main function
- returns
the dataframe containing all the anomalies
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