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This operator searches for anomalies in a sequence in comparison to the learned sequences. The data input port is 0, the input port with learn data is 1.


  • interval Defines, how many standard deviations are allowed for a tuple to be different from the mean. 3.0 is the default value. Choose a smaller value to get more anomalies
  • tupleCountLearnAttribute The attribute name on the learn port that gives the group count (the counter that gives each tuple in the sequence a number)
  • meanLearnAttribute The attribute name on the learn port that has the mean
  • standardDeviationLearnAttribute The attribute name on the learn port that has the standard deviation
  • valueDataAttribute Name of the attribute which should be analysed


 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']]              
/// The elements within one sequence will be counted (starts from 1 with each new sequence)
sequence = MAP({
              expressions = ['temp','counter(state)']                        
 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"
                  group_by = ['counter_state_'],
                  parameterAttribute = 'temp',
                  sequencesToLearn = 15                               
 Check, if the current tuple of this sequence differes from the normal 
tuples of the sequence at the specific point of the sequence
                    interval = 4.0,
                    standardDeviationLearnAttribute = 'standardDeviation',
                    group_by = ['group'],
                    meanLearnAttribute = 'mean',
                    valueDataAttribute = 'temp'                 
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