Classification of structured validation data using stateless and stateful features

To reliably identify problems impacting the service quality and system dependability of mobile communication networks, the monitored data needs to be validated. This paper proposes and evaluates analysis methods, features and learning methods for the automatic validation of such data, with a special focus on failure data of mobile communication data. This data can be analyzed for discriminating failures caused by problems in the infrastructure (valid failures) from those caused by other circumstances like device imperfections (invalid failures), with the purpose of filtering the invalid failures, which effectively increases both dependability and value of the underlying data. To represent the complex structural and temporal properties of the mobile communication data, two complementary feature representations are proposed and compared, followed by a discussion of classification methods which are suitable for these feature spaces and for an interpretation of their results to support manual auditing. Their classification performances on these feature spaces are evaluated and compared to competitive approaches. In the evaluation a classification performances of up to 97% AUC–ROC is achieved. This renders our approach a good alternative to using manual matching rules, which require costly expert-knowledge and are much more time-consuming to define and maintain — while also highlighting the relevance of combining feature spaces of different problem perspectives. Additionally it is shown that using non-proprietary data analysis can enable feature representations nearly as expressive as those created by using proprietary analysis methods, which allows a broader application of the proposed methods, due to the lower processing requirements.