Learning event detection rules with noise hidden Markov models

Abstract

Complex Event Processing (CEP) is a popular method to monitor processes in several contexts, especially when dealing with incidents at distinct points in time. Specific temporal combinations of various events are often of special interest for automatic detection. For the description of such patterns, one can either implement rules in some higher programming language or use some Event Description Language (EDL). Both is complicated and error-prone for non-engineers, because it varies greatly from natural language. Therefore, we present a method, by which a domain expert can simply signal the occurrence of a significant incident at a specific point in time. The system then infers rules for automatically detecting such occurrences later on. At the core of our approach is an extension of hidden Markov models (HMM) called noise hidden Markov models (nHMM) that can be trained with existing, low-level event data. The nHMM can be applied online without any intervention of programming experts. An evaluation on both synthetic and real event data shows the efficiency of our approach even under the presence of highly frequent, insignificant events and uncertainty in the data.