Abstract: A computer implemented method for analyzing a target system for the purpose of controlling the target system. The method is performed by obtaining (301) a dataset comprising observations related to the target system; computing (302) alignment score for the dataset using a linear kernel to obtain a linear alignment score; computing (302) alignment score for the dataset using a non-linear kernel to obtain a non-linear alignment score; comparing (303) the linear alignment score and the non-linear alignment score; and if linear alignment score>non-linear alignment score, selecting (304) anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures.

Abstract: Analyzing measurement results of a target system. Measurement results are obtained (401). The measurement results include multiple data entries, and each data entry includes multiple data values on a lowest hierarchy level and hierarchy information defining with which entities the entry is related to on different hierarchy levels. An aggregated anomaly score is determined (402) for each data entry. Data entries, wherein the aggregated anomaly score fulfils predefined criteria, are chosen for further analysis. Hierarchical clustering is performed (404) on the chosen entries based on dissimilarity of the chosen entries to combine at least some of the chosen entries together; and the hierarchically clustered entries are used (405) to identify one or more anomalous entities on hierarchy levels above the lowest hierarchy level.

Abstract: Analyzing measurement results of a target system. The analysis is performed by receiving a first matrix including first measurement results of the target system; training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results; receiving a second matrix including second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results; selecting from the third matrix a subset that matches with the second matrix; subtracting the selected subset from the second matrix to obtain a fifth matrix; outputting the fifth matrix or information derived from the fifth matrix for the purpose of evaluating performance of the target system.