A Process for Identifying Predictive Correlation Patterns in Service Management Systems
|Professorship/Faculty:||Distributed Systems||Authors:||Zirkel, Werner; Wirtz, Guido||Corporate Body:||Siemens AG, Healthcare IT, Erlangen|
|Publisher Information:||Bamberg : opus||Year of publication:||2010||Pages / Size:||I, 20 S. : graph. Darst.||Series ; Volume:||Bamberger Beiträge zur Wirtschaftsinformatik und Angewandten Informatik ; 84||Language(s):||English||Licence:||German Act on Copyright||URN:||urn:nbn:de:bvb:473-opus-2292||Document Type:||Other||Abstract:||
By using the remote functions of a modern IT service management system infrastructure, it is possible to analyze huge amounts of logfile data from complex technical equipment. This enables a service provider to predict failures of connected equipment before they happen. The problem most providers face in this context is finding a needle in a haystack - the obtained amount of data turns out to be too large to be analyzed manually. This report describes a process to find suitable predictive patterns in log files for the detection of upcoming critical situations. The identification process may serve as a hands-on guide. It describes how to connect statistical means, data mining algorithms and expert domain knowledge in the domain of service management. The process was developed in a research project which is currently being carried out within the Siemens Healthcare service organization. The project deals with two main aspects: First, the identification of predictive patterns in existing service data and second, the architecture of an autonomous agent which is able to correlate such patterns. This paper summarizes the results of the first project challenge. The identification process was tested successfully in a proof of concept for several Siemens Healthcare products.
|SWD Keywords:||Service management , Datenmanagement , Autonomer Agent ; Online-Publikation
Autonomer Agent ; Online-Publikation
|Keywords:||Service Management , Ereignis Korrelation , Pattern Identifizierung, Service Management , Event Correlation , Pattern Identification, Service Management, Ereignis Korrelation, Pattern Identifizierung, Event Correlation, Pattern Identification||DDC Classification:||004 Computer science||RVK Classification:||ST 515||URI:||https://fis.uni-bamberg.de/handle/uniba/234||Release Date:||19. April 2012|