You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 3 Next »

This operator uses the Local Outlier Factor (LOF) algorithm to find anomalies. You should use this operator with a window, because the operator saves all tuples in the current window and uses the normal LOF-algorithm on this set of tuples. If the number of tuples is too high, the algorithm may take a while to process the calculations.


  • NEIGHBORS The number of neighbors used, sometimes called k
  • LOFVALUE The value from which the tuples are declared as anomalies. Values near 1 are normal, higher values are anomalies. Standard is 1.5.
  • nameOfParameter Name of the attribute which should be analysed
  • GROUP_BY Group by the given attribute, e.g. if you have a context like 'on' and 'off' you want to analyse separately.
  • fastGrouping Use hash code instead of tuple compare to create group. Potentially unsafe!
  • ignoreEqual Set to true, if you want to ignore, if there are a lot of equal values. This can lead to false positives.
  • deliverFirstElements The operator starts to work when the window contains at least the number of neighbors + 1 tuples. If you want to get the first elements before this is reached, you can set this to true. Default is false.


PQL query for the LOF operator
/// Searches for anomalies in temperature data
temperatureWindow = ELEMENTWINDOW({
                        size = 100
                  lofvalue = 2.5,
                  neighbors = 5,
                  ignoreequal = 'true',
                  nameofparameter = 'temp',
                  deliverFirstElements = 'true'
  • No labels