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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:

Counter	Value
0	8
1	5
2	20
0	4
1	6
2	22

This were two sequences, cause a new sequence starts, if the counter of the next tuple is smaller than the previous counter. Every sequence had three tuples.

Parameters

parameterAttribute 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_'],
                  parameterAttribute = '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
                )