Algorithm design and application of service-oriented event correlation

Hanemann, Andreas; Marcu, Patricia

doi:10.1109/bdim.2008.4540075

Cited by 18 publications

(7 citation statements)

References 14 publications

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“…Some examples: Hanemann and Marcu differentiate hypotheses-based approaches such as the codebook approach, case-based reasoning and active probing. [3] Hanemann also proposes a combination of rule-based mechanisms and case-based reasoning for correlation. Besides these approaches there exist data-driven methods.…”

Section: Statistical Methods For Event Correlation and Failure Predicmentioning

confidence: 98%

“…The event management activities have changed over time with an increase in the abstraction level of service management systems, from polling simple status surveillance information to analyzing complex workflow data. [3]: This is done by using intelligent agents to retrieve complex information about the status and the workload, thereby enabling service providers to ensure the continual uptime of a service object. Because of this, the service management often uses a distributed software architecture [10] where software agents are used to read the service data and correlate the information to provide a service operator with meaningful information.…”

Section: Service Management Fundamentalsmentioning

confidence: 99%

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A process for identifying predictive correlation patterns in service management systems

Zirkel

Wirtz

2010

2010 7th International Conference on Service Systems and Service Management

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By using the remote functions of a modern IT service management system infrastructure, it is possible to analyze huge amounts of log file 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. The following 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.

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Section: Statistical Methods For Event Correlation and Failure Predicmentioning

confidence: 98%

Section: Service Management Fundamentalsmentioning

confidence: 99%

A process for identifying predictive correlation patterns in service management systems

Zirkel

Wirtz

2010

2010 7th International Conference on Service Systems and Service Management

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“…[4] proposes an algorithm for event correlation, extended in [5], based on the same service model as in [3]. Events are correlated for root-cause analysis using Rule-Based Reasoning (RBR) and active probing.…”

Section: Related Workmentioning

confidence: 99%

Towards an optimized model of incident ticket correlation

Marcu

Grabarnik

Luan

et al. 2009

2009 IFIP/IEEE International Symposium on Integrated Network Management

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In an ITS system, seemingly unrelated tickets created by end users and monitoring systems can coexist and have the same root cause. The connection between failed resource and malfunctioning services is not realized automatically, but often established manually by means of human intervention. This need for human involvement reduces productivity. The introduction of automation would increase productivity and therefore reduce the cost of incident resolutionIn this paper, we propose a model to correlate incident tickets based on three criteria. First, we employ a category-based correlation that relies on matching service identifiers with associated resource identifiers, using similarity rules. Secondly, we correlate the configuration items which are critical to the failed service with the earlier identified resource tickets in order to optimize the topological comparison. Finally, we augment scheduled resource data collection with constraint adaptive probing to minimize the correlation interval for temporally correlated tickets. We present experimental data in support of our proposed correlation model.

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“…Model bazlı uzman sistemlerde şebekenin topolojik modellerini ve şebekeden gelen alarmları kullanan uzman kurallar kullanılarak oluşan aksaklıklar bulunmaktadır. Aksaklıkların bulunmasında kullanılan bir başka yaygın yöntem örnek olay bazlı uzman sistemlerdir [5,6]. Bu yöntemde bilinen aksaklık çeşitleri ve çözüm yöntemleri bir uzman sistem üzerine aktarılarak örnek olay kütüphanesi oluşturulur.…”

Section: Introductionunclassified

Detection of periodic problems using non-parametric density estimation

Sarkan

Çataltepe

2013

2013 21st Signal Processing and Communications Applications Conference (SIU)

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ÖZETÇEGSM şebekelerinde karşılaşılan kronik kalite aksaklıklarının önemli bir kısmı belirli aralıklarla oluşan, sıkça tekrarlanıp kısa sürede ortadan kalkan, periyodik kalite alarmlarına neden olmaktadır. Şebekede oluşan kalite alarmlarının sayısı yüksek olduğu için, GSM şebekelerinde aksaklık yönetiminden sorumlu uzman personel düzenli olarak kalite alarmlarını inceleyip kronik kalite aksaklıklarını belirlemeye çalışmaktadır. Periyodik alarmlara neden olan kronik kalite aksaklıklarının uzman personel tarafından alarm tarihçesi incelenerek belirlenmesi uzun zaman alan maliyetli bir işlemdir. Bu çalışmada alarm tarihçesi üzerinde dağılımdan bağımsız olasılık kestirimi yöntemi kullanılarak periyodik alarmlara neden olan aksaklıkları belirlemek için yeni bir yöntem sunulmaktadır. Çalışma kapsamında alarm tarihçesindeki alarmlar üzerinde dağılımdan bağımsız olasılık kestirimi yaklaşımı ile alarmların kaynak, tip ve tekrarlanma bilgilerine göre histogramlar üretilmiştir. Bu histogramlar uzmanlar tarafından incelenmiş ve periyodik kalite alarmları etiketlenmiştir. Kronik kalite alarmı tespiti için kurulan bir modelin parametreleri etiketli veri kullanılarak öğrenilmiştir. Öğrenilmiş model kullanılarak kronik kalite aksaklıklarına sahip kaynaklar büyük bir başarı oranı ile bulunmuştur. Anahtar Kelimeler -dağılımdan bağımsız olasılık kestirimi; histogram; GSM; kalite problemleri; kalite aksaklıkları.ABSTRACT A significant number of chronic GSM quality faults cause chronic quality alarms, which happen periodically, repeat often and disappear after a short time. There are a huge number of quality alarms and alarm quality experts who are responsible for the fault management try to determine chronic quality faults by examining quality alarms. The task of examining the history of a huge number of alarms and determining the chronic quality faults is a very time consuming and expensive task. In this study, a new non-parametric density estimation approach is proposed to determine quality faults which cause periodic alarms. We use the alarm history to do a nonparametric density estimation using histograms, based on the source, type and reoccurrence information for each alarm. These histograms were examined by the experts and they labeled the alarm histograms based on whether they point to chronic quality problems or not. We built a model to determine chronic quality problems and learned the parameters of this model using the alarm histogram data labeled by the experts. The learned model had a very good performance in determining the chronic quality faults.

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Algorithm design and application of service-oriented event correlation

Cited by 18 publications

References 14 publications

A process for identifying predictive correlation patterns in service management systems

A process for identifying predictive correlation patterns in service management systems

Towards an optimized model of incident ticket correlation

Detection of periodic problems using non-parametric density estimation

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