Detecting Temporally Related Arithmetical Patterns An Extension of Complex Event Processing Ronald de Haan 1 and Mikhail Roshchin 2 1 Technische Universit¨ at Dresden, Dresden, Germany 2 Siemens Corporate Technologies, M¨ unchen, Germany Keywords: Complex Event Processing, Diagnostic Knowledge, Temporal Relations, Arithmetical Patterns. Abstract: When modelling diagnostic knowledge on technical systems it is often important to be able to capture com- plex events with a temporal structure, based on arithmetical patterns in (preprocessed) sensor data. With the current methods, such a combination is not easily possible. To solve this problem, we devise an extension of complex event processing methods, by designing a declarative language to specify the generation of events with a complex temporal structure that are based on arithmetical patterns in numerical data. This extension furthermore makes complex event processing methods more easily accessible for users that have no experience with complex event processing. 1 INTRODUCTION In the modelling of diagnostic knowledge of large technical systems, it is important to be able to rep- resent certain patterns in the sensor data observed in such a system. For instance, it can often be predicted on the basis of patterns in the temperature and pres- sure observations in a system such as a gas turbine that an expensive malfunction is about to take place. Often the patterns that are relevant for making such predictions are complex patterns, consisting of sim- pler patterns occurring in a particular temporal rela- tion. There are several different existing methods to approach the problem of automatically detecting such patterns. The method of complex event processing (CEP) (Luckham, 2001) is one way to handle complex tem- poral patterns of observations. Central to this method is the processing of events. An event is a message that a particular situation has occurred at a certain time point. On the one hand, events can be based on sim- ple observations. On the other hand, complex events can be based on the occurrence of simpler events in a particular temporal structure. Complex event pro- cessing allows engineers to flexibly and declaratively specify the definition of such complex events. Another approach to process observations, par- ticularly in numerical data, is to deploy arithmetical methods to detect patterns in the data. This often in- volves preprocessing and normalization. Arithmetical methods can then be used on the preprocessed data to detect situations such as increasing or decreasing trends, or the exceedance of a threshold. The prepro- cessing allows us to detect such situations in the data itself, but also in derived data such as the average or standard deviation over a given time period. 1.1 Problem Statement For expressing diagnostic knowledge of technical sys- tems often the most meaningful patterns are com- plex temporally related occurrences of observations in (preprocessed) data. CEP allows us to flexibly de- tect the complex temporal structure, while arithmeti- cal knowledge discovery methods allow us to detect the basic arithmetical patterns in the (preprocessed) data. However, an approach that allows us to combine the advantages of both methods is not yet available. In this paper, we will develop a method that makes it possible to conveniently and flexibly specify complex temporally structured events based on basic arithmetical patterns in preprocessed data. 2 APPROACH We develop a system that combines the ability of complex event processing to specify complex tempo- rally structured events with the possibility to specify arithmetical patterns in preprocessed data. 329 de Haan R. and Roshchin M.. Detecting Temporally Related Arithmetical Patterns - An Extension of Complex Event Processing. DOI: 10.5220/0004135603290332 In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2012), pages 329-332 ISBN: 978-989-8565-29-7 Copyright c  2012 SCITEPRESS (Science and Technology Publications, Lda.)