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This operator learns deviation of (each point of a) sequence. The sequence needs a counter so that the operator can distinguish multiple sequences and the values within a sequence. The operator uses the online-learn algorithm of the DeviationLearn operator.

Example

The operator gets the following input:

CounterValue
08
15
220
04
16
222

This were two sequences, because a new sequence starts if the counter of the next tuple is smaller than the previous counter. Every sequence had three tuples. The output on port 1 would be the following:

groupmeanstandardDeviation
08.00.0
15.00.0
220.00.0
06.02.828
15.50.707
221.01.414

Parameters

  • attribute Name of the attribute which should be analysed
  • sequencesToLearn The number of (correct) sequencesto learn from. The first x sequences will define the perfect sequence the others are compared to. If set to 0, the operator will not stop to learn (learn infinity sequences). Default is 0.
  • GROUP_BY To group the tuples into the single parts of the sequence.
  • fastGrouping Use hash code instead of tuple compare to create group. Potentially unsafe!

Example

The example PQL code shows, how to use the operator. The GROUP_BY parameter is very important because it is used to distinguish the single values within one sequence.

#PARSER PQL
#RUNQUERY
/// Values above 50 will be 'true' (which means that the current sequence starts / runs) and smaller values to 'false' (means: sequence ended)
stateInfo = MAP({
                expressions = ['temp', ['temp > 50', 'state']]              
              },
              System.manual
            )

/// The elements within one sequence will be counted (starts from 1 with each new sequence)
sequence = MAP({
              expressions = ['temp','counter(state)']                        
            },
            stateInfo
          )

/// The tuple which marks the end of the sequence (and itself is not part of the sequence) has the counter_state_ 0 and will be filtered out 
onlySequence = SELECT({PREDICATE = 'counter_state_ > 0'}, sequence)

/// Learn how a "normal" sequence is. The first 15 sequences will be learned and used as the definition of "normal"
sequenceLearn = DEVIATIONSEQUENCELEARN({
                  group_by = ['counter_state_'],
                  attribute = 'temp',
                  sequencesToLearn = 15                                
                },
                onlySequence
              )
              
/// Check, if the current tuple of this sequence differes from the normal tuples of the sequence at the specific point of the sequence
sequenceAnalysis = DEVIATIONSEQUENCEANOMALYDETECTION({
                    interval = 4.0,
                    standardDeviationLearnAttribute = 'standardDeviation',
                    group_by = ['group'],
                    meanLearnAttribute = 'mean',
                    valueDataAttribute = 'temp'                  
                  },
                  0:sequenceLearn,
                  1:sequenceLearn
                )
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