Knowledge discovery from telecommunication network alarm databases

Hätönen, Kimmo; Klemettinen, Mika; Mannila, Heikki; Ronkainen, Pirjo; Toivonen, Hannu

doi:10.1109/icde.1996.492095

Cited by 76 publications

(36 citation statements)

References 4 publications

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“…A KDD process, adapted from [11], consists of: We follow the general framework, but there are two important characteristics that separate the methodology from the others [1,12] 1. In the rule discovery phase, it aims to find all potentially interesting patterns according to rather loose criteria for frequency and confidence.…”

Section: Tasa Methodologymentioning

confidence: 99%

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Interactive exploration of interesting findings in the Telecommunication Network Alarm Sequence Analyzer (TASA)

Klemettinen

Mannila

Toivonen

1999

Information and Software Technology

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In this paper we describe the final version of a knowledge discovery system, Telecommunication Network Alarm Sequence Analyzer (TASA), for telecommunication networks alarm data analysis. The system is based on the discovery of recurrent, temporal patterns of alarms in databases; these patterns, episode rules, can be used in the construction of real-time alarm correlation systems. Also association rules are used for identifying relationships between alarm properties. TASA uses a methodology for knowledge discovery in databases (KDD) where one first discovers large collections of patterns at once, and then performs interactive retrievals from the collection of patterns. The proposed methodology suits very well such KDD formalisms as association and episode rules, where large collections of potentially interesting rules can be found efficiently. When searching for the most interesting rules, simple threshold-like restrictions, such as rule frequency and confidence may satisfy a large number of rules. In TASA, this problem can be alleviated by templates and pattern expressions that describe the form of rules that are to be selected or rejected. Using templates the user can flexibly specify the focus of interest, and also iteratively refine it. Different versions of TASA have been in prototype use in four telecommunication companies since the beginning of 1995. TASA has been found useful in, e.g. finding long-term, rather frequently occurring dependencies, creating an overview of a short-term alarm sequence, and evaluating the alarm data base consistency and correctness. ᭧

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Section: Tasa Methodologymentioning

confidence: 99%

“…We describe the final version of a knowledge discovery system, Telecommunication Network Alarm Sequence Analyzer (TASA) [1], that supports the methodology. TASA was built at the University of Helsinki, in cooperation with four telecommunication companies, and it has recently been successfully fielded.…”

Section: Introductionmentioning

confidence: 99%

Interactive exploration of interesting findings in the Telecommunication Network Alarm Sequence Analyzer (TASA)

Klemettinen

Mannila

Toivonen

1999

Information and Software Technology

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“…Hatonen et al [5] have used frequently occurring episodes for discovering patterns in alarm databases to predict faults. Their subsequent work TASA (Telecommunication Alarm Sequence Analyzer) [6] was a system for mining knowledge from telecommunications networks in terms of 'episode rules'.…”

Section: Related Workmentioning

confidence: 99%

Associative data mining for alarm groupings in chemical processes

Kordic¹,

Lam²,

Xiao³

et al. 2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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Complex industrial processes such as nuclear power plants, chemical plants and petroleum refineries are usually equipped with alarm systems capable of monitoring thousands of process variables and generating tens of thousands of alarms which are used as mechanisms for alerting operators to take actions to alleviate or prevent an abnormal situation. Overalarming and a lack of configuration management practices have often led to the degradation of these alarm systems, resulting in operational problems such as the Three-Mile Island accident. In order to aid alarm rationalization, this paper proposed an approach that incorporates a context-based segmentation approach with a data mining technique to find a set of correlated alarms from historical alarm event logs. Before the set of extracted results from this automated technique are used they can be evaluated by a process engineer with process understanding. The proposed approach is evaluated initially using simulation data from a Vinyl Acetate model. The approach is cost effective as any manual alarm analysis of the event logs for identifying primary and consequential alarms could be very time and labour intensive.

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“…This kind of information can be used to restructure the web-site, or to dynamically insert relevant links in web pages based on user access patterns. Other domains where sequence mining has been applied include identifying plan failures , selecting good features of classification (Lesh et al, 2000), finding network alarm patterns (Hatonen et al, 1996), and so on.…”

Section: Introductionmentioning

confidence: 99%

Sequence Mining in Categorical Domains: Algorithms and Applications

Zaki

2000

Sequence Learning

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Knowledge discovery from telecommunication network alarm databases

Cited by 76 publications

References 4 publications

Interactive exploration of interesting findings in the Telecommunication Network Alarm Sequence Analyzer (TASA)

Interactive exploration of interesting findings in the Telecommunication Network Alarm Sequence Analyzer (TASA)

Associative data mining for alarm groupings in chemical processes

Sequence Mining in Categorical Domains: Algorithms and Applications

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